Base_JSON/prefixN/json/nlpcovid19/2020.nlpcovid19-acl.1.json

{
    "paper_id": "2020",
    "header": {
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        "date_generated": "2023-01-19T14:39:27.622354Z"
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    "title": "CORD-19: The COVID-19 Open Research Dataset",
    "authors": [
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            "first": "Lucy",
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            "first": "Ziyang",
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            "first": "William",
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    "abstract": "The COVID-19 Open Research Dataset (CORD-19) is a growing 1 resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded 2 over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19. * denotes equal contribution 1 The dataset continues to be updated daily with papers from new sources and the latest publications. Statistics reported in this article are up-to-date as of version 2020-06-14. 2 https://www.semanticscholar.org/cord19",
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        "abstract": [
            {
                "text": "The COVID-19 Open Research Dataset (CORD-19) is a growing 1 resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded 2 over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19. * denotes equal contribution 1 The dataset continues to be updated daily with papers from new sources and the latest publications. Statistics reported in this article are up-to-date as of version 2020-06-14. 2 https://www.semanticscholar.org/cord19",
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                "section": "Abstract",
                "sec_num": null
            }
        ],
        "body_text": [
            {
                "text": "On March 16, 2020, the Allen Institute for AI (AI2), in collaboration with our partners at The White House Office of Science and Technology Policy (OSTP), the National Library of Medicine (NLM), the Chan Zuckerburg Initiative (CZI), Microsoft Research, and Kaggle, coordinated by Georgetown University's Center for Security and Emerging Technology (CSET), released the first version of . This resource is a large and growing collection of publications and preprints on COVID-19 and related historical coronaviruses such as SARS and MERS. The initial release consisted of 28K papers, and the collection has grown to more than 140K papers over the subsequent weeks. Papers and preprints from several archives are collected and ingested through the Semantic Scholar literature search engine, 3 metadata are harmonized and deduplicated, and paper documents are processed through the pipeline established in to extract full text (more than 50% of papers in CORD-19 have full text). We commit to providing regular updates to the dataset until an end to the COVID-19 crisis is foreseeable.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "CORD-19 aims to connect the machine learning community with biomedical domain experts and policy makers in the race to identify effective treatments and management policies for COVID-19. The goal is to harness these diverse and com-plementary pools of expertise to discover relevant information more quickly from the literature. Users of the dataset have leveraged AI-based techniques in information retrieval and natural language processing to extract useful information.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "Responses to have been overwhelmingly positive, with the dataset being downloaded over 200K times in the three months since its release. The dataset has been used by clinicians and clinical researchers to conduct systematic reviews, has been leveraged by data scientists and machine learning practitioners to construct search and extraction tools, and is being used as the foundation for several successful shared tasks. We summarize research and shared tasks in Section 4.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "In this article, we briefly describe:",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "1. The content and creation of 2 . Design decisions and challenges around creating the dataset, 3. Research conducted on the dataset, and how shared tasks have facilitated this research, and 4. A roadmap for CORD-19 going forward.",
                "cite_spans": [
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                        "start": 31,
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                        "text": "2",
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "CORD-19 integrates papers and preprints from several sources (Figure 1) , where a paper is defined as the base unit of published knowledge, and a preprint as an unpublished but publicly available counterpart of a paper. Throughout the rest of Section 2, we discuss papers, though the same processing steps are adopted for preprints. First, we ingest into Semantic Scholar paper metadata and documents from each source. Each paper is associated with bibliographic metadata, like title, authors, publication venue, etc, as well as unique identifiers such as a DOI, PubMed Central ID, PubMed ID, the WHO Covidence #, 4 MAG identifier (Shen et al., 2018) , and others. Some papers are associated with documents, the physical artifacts containing paper content; these are the familiar PDFs, XMLs, or physical print-outs we read.",
                "cite_spans": [
                    {
                        "start": 631,
                        "end": 650,
                        "text": "(Shen et al., 2018)",
                        "ref_id": "BIBREF30"
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                "ref_spans": [
                    {
                        "start": 61,
                        "end": 71,
                        "text": "(Figure 1)",
                        "ref_id": "FIGREF0"
                    }
                ],
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                "section": "Dataset",
                "sec_num": "2"
            },
            {
                "text": "For the CORD-19 effort, we generate harmonized and deduplicated metadata as well as structured full text parses of paper documents as output. We provide full text parses in cases where we have access to the paper documents, and where the documents are available under an open access license (e.g. Creative Commons (CC), 5 publisher-specific COVID-19 licenses, 6 or identified as open access through DOI lookup in the Unpaywall 7 database).",
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                "section": "Dataset",
                "sec_num": "2"
            },
            {
                "text": "Papers in CORD-19 are sourced from PubMed Central (PMC), PubMed, the World Health Organization's Covid-19 Database, 4 and preprint servers bioRxiv, medRxiv, and arXiv. The PMC Public Health Emergency Covid-19 Initiative 6 expanded access to COVID-19 literature by working with publishers to make coronavirus-related papers discoverable and accessible through PMC under open access license terms that allow for reuse and secondary analysis. BioRxiv and medRxiv preprints were initially provided by CZI, and are now ingested through Semantic Scholar along with all other included sources. We also work directly with publishers such as Elsevier 8 and Springer Nature, 9 to provide full text coverage of relevant papers available in their back catalog.",
                "cite_spans": [],
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                "section": "Sources of papers",
                "sec_num": "2.1"
            },
            {
                "text": "All papers are retrieved given the query 10 :",
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                "section": "Sources of papers",
                "sec_num": "2.1"
            },
            {
                "text": "\"COVID\" OR \"COVID-19\" OR \"Coronavirus\" OR \"Corona virus\" OR \"2019-nCoV\" OR \"SARS-CoV\" OR \"MERS-CoV\" OR \"Severe Acute Respiratory Syndrome\" OR \"Middle East Respiratory Syndrome\"",
                "cite_spans": [],
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                "section": "Sources of papers",
                "sec_num": "2.1"
            },
            {
                "text": "Papers that match on these keywords in their title, abstract, or body text are included in the dataset. Query expansion is performed by PMC on these search terms, affecting the subset of papers in CORD-19 retrieved from PMC.",
                "cite_spans": [],
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                "section": "Sources of papers",
                "sec_num": "2.1"
            },
            {
                "text": "The initial collection of sourced papers suffers from duplication and incomplete or conflicting metadata. We perform the following operations to harmonize and deduplicate all metadata:",
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                "section": "Processing metadata",
                "sec_num": "2.2"
            },
            {
                "text": "1. Cluster papers using paper identifiers 2. Select canonical metadata for each cluster 3. Filter clusters to remove unwanted entries",
                "cite_spans": [],
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                "section": "Processing metadata",
                "sec_num": "2.2"
            },
            {
                "text": "Clustering papers We cluster papers if they overlap on any of the following identifiers: {doi, pmc id, pubmed id, arxiv id, who covidence id, mag id}. If two papers from different sources have an identifier in common and no other identifier conflicts between them, we assign them to the same cluster. Each cluster is assigned a unique identifier CORD UID, which persists between dataset releases. No existing identifier, such as DOI or PMC ID, is sufficient as the primary CORD-19 identifier. Some papers in PMC do not have DOIs; some papers from the WHO, publishers, or preprint servers like arXiv do not have PMC IDs or DOIs. Occasionally, conflicts occur. For example, a paper c with (doi, pmc id, pubmed id) identifiers (x, null, z ) might share identifier x with a cluster of papers {a, b} that has identifiers (x, y, z), but has a conflict z = z. In this case, we choose to create a new cluster {c}, containing only paper c. 11",
                "cite_spans": [],
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                "section": "Processing metadata",
                "sec_num": "2.2"
            },
            {
                "text": "Selecting canonical metadata Among each cluster, the canonical entry is selected to prioritize the availability of document files and the most permissive license. For example, between two papers with PDFs, one available under a CC license and one under a more restrictive COVID-19-specific copyright license, we select the CC-licensed paper entry as canonical. If any metadata in the canonical entry are missing, values from other members of the cluster are promoted to fill in the blanks.",
                "cite_spans": [],
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                "section": "Processing metadata",
                "sec_num": "2.2"
            },
            {
                "text": "Cluster filtering Some entries harvested from sources are not papers, and instead correspond to materials like tables of contents, indices, or informational documents. These entries are identified in an ad hoc manner and removed from the dataset.",
                "cite_spans": [],
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                "section": "Processing metadata",
                "sec_num": "2.2"
            },
            {
                "text": "Most papers are associated with one or more PDFs. 12 To extract full text and bibliographies from each PDF, we use the PDF parsing pipeline created for the S2ORC dataset . 13 In , we introduce the S2ORC JSON format for representing scientific paper full text, which is used as the target output for paper full text in CORD-19. The pipeline involves:",
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                "section": "Processing full text",
                "sec_num": "2.3"
            },
            {
                "text": "1. Parse all PDFs to TEI XML files using GRO-BID 15 (Lopez, 2009) 2. Parse all TEI XML files to S2ORC JSON 3. Postprocess to clean up links between inline citations and bibliography entries.",
                "cite_spans": [
                    {
                        "start": 52,
                        "end": 65,
                        "text": "(Lopez, 2009)",
                        "ref_id": "BIBREF19"
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                ],
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                "section": "Processing full text",
                "sec_num": "2.3"
            },
            {
                "text": "We additionally parse JATS XML 16 files available for PMC papers using a custom parser, generating the same target S2ORC JSON format. This creates two sets of full text JSON parses associated with the papers in the collection, one set originating from PDFs (available from more sources), and one set originating from JATS XML (available only for PMC papers). Each PDF parse has an associated SHA, the 40-digit SHA-1 of the associated PDF file, while each XML parse is named using its associated PMC ID. Around 48% of CORD-19 papers have an associated PDF parse, and around 37% have an XML parse, with the latter nearly a subset of the former. Most PDFs (>90%) are successfully parsed. Around 2.6% of CORD-19 papers are associated with multiple PDF SHA, due to a combination of paper clustering and the existence of supplementary PDF files.",
                "cite_spans": [],
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                "section": "Processing full text",
                "sec_num": "2.3"
            },
            {
                "text": "Since the May 12, 2020 release of CORD-19, we also release selected HTML table parses. Tables contain important numeric and descriptive information such as sample sizes and results, which are the targets of many information extraction systems. A separate PDF table processing pipeline  is used, consisting of table extraction and table  understanding. Table extraction is based on the Smart Document Understanding (SDU) capability included in IBM Watson Discovery. 17 SDU converts a given PDF document from its native binary representation into a text-based representation like HTML which includes both identified document structures (e.g., tables, section headings, lists) and formatting information (e.g. positions for extracted text). ",
                "cite_spans": [],
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                        "start": 255,
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                        "text": "PDF table processing pipeline  is used, consisting of table extraction and table  understanding. Table extraction",
                        "ref_id": null
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                ],
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                "section": "Table parsing",
                "sec_num": "2.4"
            },
            {
                "text": "All PDFs are processed through this table extraction and understanding pipeline. If the Jaccard similarity of the table captions from the table parses and CORD-19 parses is above 0.9, we insert the HTML of the matched table into the full text JSON. We extract 188K tables from 54K documents, of which 33K tables are successfully matched to tables in 19K (around 25%) full text documents in CORD-19. Based on preliminary error analysis, we find that match failures are primarily due to caption mismatches between the two parse schemes. Thus, we plan to explore alternate matching functions, potentially leveraging table content and document location as additional features. See Appendix A for example table parses.",
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                "section": "2020), which uses a specialized object detection and clustering technique to extract table bounding boxes and structures.",
                "sec_num": null
            },
            {
                "text": "CORD-19 has grown rapidly, now consisting of over 140K papers with over 72K full texts. Over 47K papers and 7K preprints on COVID-19 and coronaviruses have been released since the start of 2020, comprising nearly 40% of papers in the dataset.",
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                "section": "Dataset contents",
                "sec_num": "2.5"
            },
            {
                "text": "Classification of CORD-19 papers to Microsoft Academic Graph (MAG) (Wang et al., 2019 fields of study (Shen et al., 2018) indicate that the dataset consists predominantly of papers in Medicine (55%), Biology (31%), and Chemistry (3%), which together constitute almost 90% of the corpus. 18 subfields (L1 fields of study) represented in CORD-19 is given in Table 1 . Figure 2 shows the distribution of CORD-19 papers by date of publication. Coronavirus publications increased during and following the SARS and MERS epidemics, but the number of papers published in the early months of 2020 exploded in response to the COVID-19 epidemic. Using author affiliations in MAG, we identify the countries from which the research in CORD-19 is conducted. Large proportions of CORD-19 papers are associated with institutions based in the Americas (around 48K papers), Europe (over 35K papers), and Asia (over 30K papers).",
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                        "start": 67,
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                        "text": "(Wang et al., 2019",
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                        "text": "(Shen et al., 2018)",
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                        "text": "18",
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                        "start": 356,
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                        "text": "Table 1",
                        "ref_id": "TABREF2"
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                        "start": 366,
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                        "text": "Figure 2",
                        "ref_id": "FIGREF1"
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                "section": "Dataset contents",
                "sec_num": "2.5"
            },
            {
                "text": "A number of challenges come into play in the creation of CORD-19. We summarize the primary design requirements of the dataset, along with challenges implicit within each requirement:",
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                "section": "Design decision & challenges",
                "sec_num": "3"
            },
            {
                "text": "Up-to-date Hundreds of new publications on COVID-19 are released every day, and a dataset like CORD-19 can quickly become irrelevant without regular updates. CORD-19 has been updated daily since May 26. A processing pipeline that produces consistent results day to day is vital to maintaining a changing dataset. That is, the metadata and full text parsing results must be reproducible, identifiers must be persistent between releases, and changes or new features should ideally be compatible with previous versions of the dataset.",
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                "section": "Design decision & challenges",
                "sec_num": "3"
            },
            {
                "text": "Handles data from multiple sources Papers from different sources must be integrated and harmonized. Each source has its own metadata format, which must be converted to the CORD-19 format, while addressing any missing or extraneous fields. The processing pipeline must also be flexible to adding new sources.",
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                "section": "Design decision & challenges",
                "sec_num": "3"
            },
            {
                "text": "on the CORD-19 landing page.",
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                "section": "Design decision & challenges",
                "sec_num": "3"
            },
            {
                "text": "Clean canonical metadata Because of the diversity of paper sources, duplication is unavoidable. Once paper metadata from each source is cleaned and organized into CORD-19 format, we apply the deduplication logic described in Section 2.2 to identify similar paper entries from different sources. We apply a conservative clustering algorithm, combining papers only when they have shared identifiers but no conflicts between any particular class of identifiers. We justify this because it is less harmful to retain a few duplicate papers than to remove a document that is potentially unique and useful.",
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                "section": "Design decision & challenges",
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            },
            {
                "text": "Machine readable full text To provide accessible and canonical structured full text, we parse content from PDFs and associated paper documents. The full text is represented in S2ORC JSON format , a schema designed to preserve most relevant paper structures such as paragraph breaks, section headers, inline references, and citations. S2ORC JSON is simple to use for many NLP tasks, where character-level indices are often employed for annotation of relevant entities or spans. The text and annotation representations in S2ORC share similarities with BioC (Comeau et al., 2019), a JSON schema introduced by the BioCreative community for shareable annotations, with both formats leveraging the flexibility of characterbased span annotations. However, S2ORC JSON also provides a schema for representing other components of a paper, such as its metadata fields, bibliography entries, and reference objects for figures, tables, and equations. We leverage this flexible and somewhat complete representation of S2ORC JSON for CORD-19. We recognize that converting between PDF or XML to JSON is lossy. However, the benefits of a standard structured format, and the ability to reuse and share annotations made on top of that format have been critical to the success of CORD-19.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Design decision & challenges",
                "sec_num": "3"
            },
            {
                "text": "Observes copyright restrictions Papers in CORD-19 and academic papers more broadly are made available under a variety of copyright licenses. These licenses can restrict or limit the abilities of organizations such as AI2 from redistributing their content freely. Although much of the COVID-19 literature has been made open access by publishers, the provisions on these open access licenses differ greatly across papers. Additionally, many open access licenses grant the ability to read, or \"consume\" the paper, but may be restrictive in Figure 3 : An example information retrieval and extraction system using CORD-19: Given an input query, the system identifies relevant papers (yellow highlighted rows) and extracts text snippets from the full text JSONs as supporting evidence.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 537,
                        "end": 545,
                        "text": "Figure 3",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Design decision & challenges",
                "sec_num": "3"
            },
            {
                "text": "other ways, for example, by not allowing republication of a paper or its redistribution for commercial purposes. The curator of a dataset like CORD-19 must pass on best-to-our-knowledge licensing information to the end user.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Design decision & challenges",
                "sec_num": "3"
            },
            {
                "text": "We provide a survey of various ways researchers have made use of CORD-19. We organize these into four categories: (i) direct usage by clinicians and clinical researchers ( \u00a74.1), (ii) tools and systems to assist clinicians ( \u00a74.2), (iii) research to support further text mining and NLP research ( \u00a74.3), and (iv) shared tasks and competitions ( \u00a74.4).",
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                "section": "Research directions",
                "sec_num": "4"
            },
            {
                "text": "CORD-19 has been used by medical experts as a paper collection for conducting systematic reviews. These reviews address questions about COVID-19 include infection and mortality rates in different demographics , symptoms of the disease (Parasa et al., 2020) , identifying suitable drugs for repurposing (Sadegh et al., 2020) , management policies (Yaacoub et al., 2020) , and interactions with other diseases (Crisan-Dabija et al., 2020; Popa et al., 2020).",
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                    {
                        "start": 235,
                        "end": 256,
                        "text": "(Parasa et al., 2020)",
                        "ref_id": "BIBREF23"
                    },
                    {
                        "start": 302,
                        "end": 323,
                        "text": "(Sadegh et al., 2020)",
                        "ref_id": "BIBREF28"
                    },
                    {
                        "start": 346,
                        "end": 368,
                        "text": "(Yaacoub et al., 2020)",
                        "ref_id": "BIBREF38"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Usage by clinical researchers",
                "sec_num": "4.1"
            },
            {
                "text": "Challenges for clinicians and clinical researchers during the current epidemic include (i) keeping up to to date with recent papers about COVID-19, (ii) identifying useful papers from historical coronavirus literature, (iii) extracting useful information from the literature, and (iv) synthesizing knowledge from the literature. To facilitate solutions to these challenges, dozens of tools and systems over CORD-19 have already been developed. Most combine elements of text-based information retrieval and extraction, as illustrated in Figure 3 . We have compiled a list of these efforts on the CORD-19 public GitHub repository 19 and highlight some systems in Table 2 . 20",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 536,
                        "end": 544,
                        "text": "Figure 3",
                        "ref_id": null
                    },
                    {
                        "start": 661,
                        "end": 668,
                        "text": "Table 2",
                        "ref_id": "TABREF4"
                    }
                ],
                "eq_spans": [],
                "section": "Tools for clinicians",
                "sec_num": "4.2"
            },
            {
                "text": "The following is a summary of resources released by the NLP community on top of CORD-19 to support other research activities.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Text mining and NLP research",
                "sec_num": "4.3"
            },
            {
                "text": "Information extraction To support extractive systems, NER and entity linking of biomedical entities can be useful. NER and linking can be performed using NLP toolkits like ScispaCy (Neumann et al., 2019) or language models like BioBERT-base and SciBERTbase finetuned on biomedical NER datasets. augments CORD-19 full text with entity mentions predicted from several techniques, including weak supervision using the NLM's Unified Medical Language System (UMLS) Metathesaurus (Bodenreider, 2004) .",
                "cite_spans": [
                    {
                        "start": 181,
                        "end": 203,
                        "text": "(Neumann et al., 2019)",
                        "ref_id": "BIBREF22"
                    },
                    {
                        "start": 474,
                        "end": 493,
                        "text": "(Bodenreider, 2004)",
                        "ref_id": "BIBREF2"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Text mining and NLP research",
                "sec_num": "4.3"
            },
            {
                "text": "Text classification Some efforts focus on extracting sentences or passages of interest. For example, Liang and Xie (2020) uses BERT (Devlin et al., 2019) to extract sentences from CORD-19 that contain COVID-19-related radiological findings.",
                "cite_spans": [
                    {
                        "start": 132,
                        "end": 153,
                        "text": "(Devlin et al., 2019)",
                        "ref_id": "BIBREF8"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Text mining and NLP research",
                "sec_num": "4.3"
            },
            {
                "text": "Pretrained model weights BioBERT and SciB-ERT have been popular pretrained LMs for COVID-19-related tasks. DeepSet has released a BERTbase model pretrained on CORD-19. 21 SPECTER (Cohan et al., 2020) paper embeddings computed using paper titles and abstracts are being released with each CORD-19 update. SeVeN relation embeddings (Espinosa-Anke and Schockaert, 2018) between word pairs have also been made available for Knowledge graphs The Covid Graph project 23 releases a COVID-19 knowledge graph built from mining several public data sources, including CORD-19, and is perhaps the largest current initiative in this space. Ahamed and Samad (2020) rely on entity co-occurrences in CORD-19 to construct a graph that enables centrality-based ranking of drugs, pathogens, and biomolecules.",
                "cite_spans": [
                    {
                        "start": 179,
                        "end": 199,
                        "text": "(Cohan et al., 2020)",
                        "ref_id": "BIBREF5"
                    },
                    {
                        "start": 330,
                        "end": 366,
                        "text": "(Espinosa-Anke and Schockaert, 2018)",
                        "ref_id": "BIBREF9"
                    },
                    {
                        "start": 627,
                        "end": 650,
                        "text": "Ahamed and Samad (2020)",
                        "ref_id": "BIBREF0"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Text mining and NLP research",
                "sec_num": "4.3"
            },
            {
                "text": "The adoption of CORD-19 and the proliferation of text mining and NLP systems built on top of the dataset are supported by several COVID-19-related competitions and shared tasks.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Competitions and Shared Tasks",
                "sec_num": "4.4"
            },
            {
                "text": "Kaggle hosts the CORD-19 Research Challenge, 24 a text-mining challenge that tasks participants with extracting answers to key scientific questions about COVID-19 from the papers in the CORD-19 dataset. Round 1 was initiated with a set of open-ended questions, e.g., What is known about transmission, incubation, and environmental stability? and What do we know about COVID-19 risk factors?",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Kaggle",
                "sec_num": "4.4.1"
            },
            {
                "text": "More than 500 teams participated in Round 1 of the Kaggle competition. Feedback from medical experts during Round 1 identified that the most useful contributions took the form of article summary tables. Round 2 subsequently focused on this task of table completion, and resulted in 100 additional submissions. A unique tabular schema is defined for each question, and answers are collected from across different automated extractions. For example, extractions for risk factors should include disease severity and fatality metrics, while extractions for incubation should include time ranges. Sufficient knowledge of COVID-19 is necessary to define these schema, to understand which fields are important to include (and exclude), and also to perform error-checking and manual curation.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Kaggle",
                "sec_num": "4.4.1"
            },
            {
                "text": "The TREC-COVID 25 shared task assesses systems on their ability to rank papers in CORD-19 based on their relevance to COVID-19-related topics. Topics are sourced from MedlinePlus searches, Twitter conversations, library searches at OHSU, as well as from direct conversations with researchers, reflecting actual queries made by the community. To emulate real-world surge in publications and rapidly- ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "TREC",
                "sec_num": "4.4.2"
            },
            {
                "text": "Several hundred new papers on COVID-19 are now being published every day. Automated methods are needed to analyze and synthesize information over this large quantity of content. The computing community has risen to the occasion, but it is clear that there is a critical need for better infrastructure to incorporate human judgments in the loop. Extractions need expert vetting, and search engines and systems must be designed to serve users. Successful engagement and usage of CORD-19 speaks to our ability to bridge computing and biomedical communities over a common, global cause. From early results of the Kaggle challenge, we have learned which formats are conducive to collaboration, and which questions are the most urgent to answer. However, there is significant work that remains for determining (i) which methods are best to assist textual discovery over the literature, (ii) how best to involve expert curators in the pipeline, and (iii) which extracted results convert to successful COVID-19 treatments and management policies. Shared tasks and challenges, as well as continued analysis and synthesis of feedback will hopefully provide answers to these outstanding questions.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Discussion",
                "sec_num": "5"
            },
            {
                "text": "Since the initial release of CORD-19, we have implemented several new features based on com-munity feedback, such as the inclusion of unique identifiers for papers, table parses, more sources, and daily updates. Most substantial outlying features requests have been implemented or addressed at this time. We will continue to update the dataset with more sources of papers and newly published literature as resources permit.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Discussion",
                "sec_num": "5"
            },
            {
                "text": "Though we aim to be comprehensive, does not cover many relevant scientific documents on COVID-19. We have restricted ourselves to research papers and preprints, and do not incorporate other types of documents, such as technical reports, white papers, informational publications by governmental bodies, and more. Including these documents is outside the current scope of CORD-19, but we encourage other groups to curate and publish such datasets.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Limitations",
                "sec_num": "5.1"
            },
            {
                "text": "Within the scope of scientific papers, CORD-19 is also incomplete, though we continue to prioritize the addition of new sources. This has motivated the creation of other corpora supporting COVID-19 NLP, such as LitCovid , which provide complementary materials to CORD-19 derived from PubMed. Though we have since added PubMed as a source of papers in CORD-19, there are other domains such as the social sciences that are not currently represented, and we hope to incorporate these works as part of future work.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Limitations",
                "sec_num": "5.1"
            },
            {
                "text": "We also note the shortage of foreign language papers in CORD-19, especially Chinese language papers produced during the early stages of the epidemic. These papers may be useful to many researchers, and we are working with collaborators to provide them as supplementary data. However, challenges in both sourcing and licensing these papers for re-publication are additional hurdles.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Limitations",
                "sec_num": "5.1"
            },
            {
                "text": "Though the full text of many scientific papers are available to researchers through CORD-19, a number of challenges prevent easy application of NLP and text mining techniques to these papers. First, the primary distribution format of scientific papers -PDF -is not amenable to text processing. The PDF file format is designed to share electronic documents rendered faithfully for reading and printing, and mixes visual with semantic information. Significant effort is needed to coerce PDF into a format more amenable to text mining, such as JATS XML, 26 BioC (Comeau et al., 2019) , or S2ORC JSON , which is used in CORD-19. Though there is substantial work in this domain, we can still benefit from better PDF parsing tools for scientific documents. As a complement, scientific papers should also be made available in a structured format like JSON, XML, or HTML.",
                "cite_spans": [
                    {
                        "start": 546,
                        "end": 580,
                        "text": "XML, 26 BioC (Comeau et al., 2019)",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Call to action",
                "sec_num": "5.2"
            },
            {
                "text": "Second, there is a clear need for more scientific content to be made accessible to researchers. Some publishers have made COVID-19 papers openly available during this time, but both the duration and scope of these epidemic-specific licenses are unclear. Papers describing research in related areas (e.g., on other infectious diseases, or relevant biological pathways) have also not been made open access, and are therefore unavailable in or otherwise. Securing release rights for papers not yet in CORD-19 but relevant for COVID-19 research is a significant portion of future work, led by the PMC COVID-19 Initiative. 6 Lastly, there is no standard format for representing paper metadata. Existing schemas like the JATS XML NISO standard 26 or library science standards like BIBFRAME 27 or Dublin Core 28 have been adopted to represent paper metadata. However, these standards can be too coarse-grained to capture all necessary paper metadata elements, or may lack a strict schema, causing representations to vary greatly across publishers who use them. To improve metadata coherence across sources, the community must define and agree upon an appropriate standard of representation.",
                "cite_spans": [
                    {
                        "start": 618,
                        "end": 619,
                        "text": "6",
                        "ref_id": null
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Call to action",
                "sec_num": "5.2"
            },
            {
                "text": "This project offers a paradigm of how the community can use machine learning to advance scientific research. By allowing computational access to the papers in CORD-19, we increase our ability to perform discovery over these texts. We hope the dataset and projects built on the dataset will serve as a template for future work in this area. We also believe there are substantial improvements that can be made in the ways we publish, share, and work with scientific papers. We offer a few suggestions that could dramatically increase community productivity, reduce redundant effort, and result in better discovery and understanding of the scientific literature.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Summary",
                "sec_num": null
            },
            {
                "text": "Through CORD-19, we have learned the importance of bringing together different communities around the same scientific cause. It is clearer than ever that automated text analysis is not the solution, but rather one tool among many that can be directed to combat the COVID-19 epidemic. Crucially, the systems and tools we build must be designed to serve a use case, whether that's improving information retrieval for clinicians and medical professionals, summarizing the conclusions of the latest observational research or clinical trials, or converting these learnings to a format that is easily digestible by healthcare consumers.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Summary",
                "sec_num": null
            },
            {
                "text": "https://semanticscholar.org/",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://www.who.int/emergencies/diseases/novelcoronavirus-2019/global-research-on-novel-coronavirus-2019-ncov",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://creativecommons.org/ 6 https://www.ncbi.nlm.nih.gov/pmc/about/covid-19/ 7 https://unpaywall.org/ 8 https://www.elsevier.com/connect/coronavirusinformation-center 9 https://www.springernature.com/gp/researchers/ campaigns/coronavirus 10 Adapted from the Elsevier COVID-19 site 8",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "This is a conservative clustering policy in which any metadata conflict prohibits clustering. An alternative policy would be to cluster if any identifier matches, under which a, b, and c would form one cluster with identifiers (x, y, [z, z ]).12 PMC papers can have multiple associated PDFs per paper, separating the main text from supplementary materials.13 One major difference in full text parsing for CORD-19 is that we do not use ScienceParse, 14 as we always derive this metadata from the sources directly.14 https://github.com/allenai/science-parse",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://github.com/kermitt2/grobid 16 https://jats.nlm.nih.gov/ 17 https://www.ibm.com/cloud/watson-discovery",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://github.com/allenai/cord19 20 There are many Search and QA systems to survey. We have chosen to highlight the systems that were made publiclyavailable within a few weeks of the CORD-19 initial release.21 https://huggingface.co/deepset/covid bert base 22 https://github.com/luisespinosaanke/cord-19-seven 23 https://covidgraph.org/",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge 25 https://ir.nist.gov/covidSubmit/index.html",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://www.niso.org/publications/z3996-2019-jats 27 https://www.loc.gov/bibframe/ 28 https://www.dublincore.org/specifications/dublincore/dces/",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "This work was supported in part by NSF Convergence Accelerator award 1936940, ONR grant N00014-18-1-2193, and the University of Washington WRF/Cable Professorship.We thank The White House Office of Science and Technology Policy, the National Library of Medicine at the National Institutes of Health, Microsoft Research, Chan Zuckerberg Initiative, and Georgetown University's Center for Security and Emerging Technology for co-organizing the CORD-19 initiative. We thank Michael Kratsios, the Chief Technology Officer of the United States, and The White House Office of Science and Technology Policy for providing the initial seed set of questions for the Kaggle CORD-19 research challenge.We thank Kaggle for coordinating the CORD-19 research challenge. In particular, we acknowledge Anthony Goldbloom for providing feedback on CORD-19 and for involving us in discussions around the Kaggle literature review tables project. We thank the National Institute of Standards and Technology (NIST), National Library of Medicine (NLM), Oregon Health and Science University (OHSU), and University of Texas Health Science Center at Houston (UTHealth) for coorganizing the TREC-COVID shared task. In particular, we thank our co-organizers -Steven Bedrick (OHSU), Aaron Cohen (OHSU), Dina Demner-Fushman (NLM), William Hersh (OHSU), Kirk Roberts (UTHealth), Ian Soboroff (NIST), and Ellen Voorhees (NIST) -for feedback on the design of CORD-19.We acknowledge our partners at Elsevier and Springer Nature for providing additional full text coverage of papers included in the corpus.We thank Bryan Newbold from the Internet Archive for providing feedback on data quality and helpful comments on early drafts of the manuscript.We thank Rok Jun Lee, Hrishikesh Sathe, Dhaval Sonawane and Sudarshan Thitte from IBM Watson AI for their help in table parsing.We also acknowledge and thank our collaborators from AI2: Paul Sayre and Sam Skjonsberg for providing front-end support for CORD-19 and TREC-COVID, Michael Schmitz for setting up the CORD-19 Discourse community forums, Adriana Dunn for creating webpage content and marketing, Linda Wagner for collecting community feedback, Jonathan Borchardt, Doug Downey, Tom Hope, Daniel King, and Gabriel Stanovsky for contributing supplemental data to the CORD-19 effort, Alex Schokking for his work on the Semantic Scholar COVID-19 Research Feed, Darrell Plessas for technical support, and Carissa Schoenick for help with public relations.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledgments",
                "sec_num": null
            },
            {
                "text": "There is high variance in the representation of tables across different paper PDFs. The goal of table parsing is to extract all tables from PDFs and represent them in HTML table format, along with associated titles and headings. In ",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "A Table parsing results",
                "sec_num": null
            }
        ],
        "bib_entries": {
            "BIBREF0": {
                "ref_id": "b0",
                "title": "Information mining for covid-19 research from a large volume of scientific literature",
                "authors": [
                    {
                        "first": "Sabber",
                        "middle": [],
                        "last": "Ahamed",
                        "suffix": ""
                    },
                    {
                        "first": "Manar",
                        "middle": [
                            "D"
                        ],
                        "last": "Samad",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "ArXiv",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Sabber Ahamed and Manar D. Samad. 2020. Informa- tion mining for covid-19 research from a large vol- ume of scientific literature. ArXiv, abs/2004.02085.",
                "links": null
            },
            "BIBREF1": {
                "ref_id": "b1",
                "title": "SciB-ERT: A pretrained language model for scientific text",
                "authors": [
                    {
                        "first": "Iz",
                        "middle": [],
                        "last": "Beltagy",
                        "suffix": ""
                    },
                    {
                        "first": "Kyle",
                        "middle": [],
                        "last": "Lo",
                        "suffix": ""
                    },
                    {
                        "first": "Arman",
                        "middle": [],
                        "last": "Cohan",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
                "volume": "",
                "issue": "",
                "pages": "3615--3620",
                "other_ids": {
                    "DOI": [
                        "10.18653/v1/D19-1371"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Iz Beltagy, Kyle Lo, and Arman Cohan. 2019. SciB- ERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan- guage Processing (EMNLP-IJCNLP), pages 3615- 3620, Hong Kong, China. Association for Computa- tional Linguistics.",
                "links": null
            },
            "BIBREF2": {
                "ref_id": "b2",
                "title": "The unified medical language system (umls): integrating biomedical terminology. Nucleic acids research, 32 Database issue",
                "authors": [
                    {
                        "first": "Olivier",
                        "middle": [],
                        "last": "Bodenreider",
                        "suffix": ""
                    }
                ],
                "year": 2004,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "267--70",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Olivier Bodenreider. 2004. The unified medical lan- guage system (umls): integrating biomedical ter- minology. Nucleic acids research, 32 Database issue:D267-70.",
                "links": null
            },
            "BIBREF3": {
                "ref_id": "b3",
                "title": "Biosentvec: creating sentence embeddings for biomedical texts",
                "authors": [
                    {
                        "first": "Q",
                        "middle": [],
                        "last": "Chen",
                        "suffix": ""
                    },
                    {
                        "first": "Y",
                        "middle": [],
                        "last": "Peng",
                        "suffix": ""
                    },
                    {
                        "first": "Z",
                        "middle": [],
                        "last": "Lu",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "2019 IEEE International Conference on Healthcare Informatics (ICHI)",
                "volume": "",
                "issue": "",
                "pages": "1--5",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Q. Chen, Y. Peng, and Z. Lu. 2019. Biosentvec: cre- ating sentence embeddings for biomedical texts. In 2019 IEEE International Conference on Healthcare Informatics (ICHI), pages 1-5.",
                "links": null
            },
            "BIBREF4": {
                "ref_id": "b4",
                "title": "Keep up with the latest coronavirus research",
                "authors": [
                    {
                        "first": "Qingyu",
                        "middle": [],
                        "last": "Chen",
                        "suffix": ""
                    },
                    {
                        "first": "Alexis",
                        "middle": [],
                        "last": "Allot",
                        "suffix": ""
                    },
                    {
                        "first": "Zhiyong",
                        "middle": [],
                        "last": "Lu",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "Nature",
                "volume": "579",
                "issue": "",
                "pages": "193--193",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Qingyu Chen, Alexis Allot, and Zhiyong Lu. 2020. Keep up with the latest coronavirus research. Na- ture, 579:193 -193.",
                "links": null
            },
            "BIBREF5": {
                "ref_id": "b5",
                "title": "Specter: Document-level representation learning using citation-informed transformers",
                "authors": [
                    {
                        "first": "Arman",
                        "middle": [],
                        "last": "Cohan",
                        "suffix": ""
                    },
                    {
                        "first": "Sergey",
                        "middle": [],
                        "last": "Feldman",
                        "suffix": ""
                    },
                    {
                        "first": "Iz",
                        "middle": [],
                        "last": "Beltagy",
                        "suffix": ""
                    },
                    {
                        "first": "Doug",
                        "middle": [],
                        "last": "Downey",
                        "suffix": ""
                    },
                    {
                        "first": "Daniel",
                        "middle": [
                            "S"
                        ],
                        "last": "Weld",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "ACL",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S. Weld. 2020. Specter: Document-level representation learning using citation-informed transformers. In ACL.",
                "links": null
            },
            "BIBREF6": {
                "ref_id": "b6",
                "title": "Pmc text mining subset in bioc: about three million full-text articles and growing",
                "authors": [
                    {
                        "first": "C",
                        "middle": [],
                        "last": "Donald",
                        "suffix": ""
                    },
                    {
                        "first": "Chih-Hsuan",
                        "middle": [],
                        "last": "Comeau",
                        "suffix": ""
                    },
                    {
                        "first": "Rezarta",
                        "middle": [],
                        "last": "Wei",
                        "suffix": ""
                    },
                    {
                        "first": "Zhiyong",
                        "middle": [],
                        "last": "Islamaj Dogan",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Lu",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "Bioinformatics",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Donald C. Comeau, Chih-Hsuan Wei, Rezarta Islamaj Dogan, and Zhiyong Lu. 2019. Pmc text mining sub- set in bioc: about three million full-text articles and growing. Bioinformatics.",
                "links": null
            },
            "BIBREF8": {
                "ref_id": "b8",
                "title": "BERT: Pre-training of deep bidirectional transformers for language understanding",
                "authors": [
                    {
                        "first": "Jacob",
                        "middle": [],
                        "last": "Devlin",
                        "suffix": ""
                    },
                    {
                        "first": "Ming-Wei",
                        "middle": [],
                        "last": "Chang",
                        "suffix": ""
                    },
                    {
                        "first": "Kenton",
                        "middle": [],
                        "last": "Lee",
                        "suffix": ""
                    },
                    {
                        "first": "Kristina",
                        "middle": [],
                        "last": "Toutanova",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
                "volume": "1",
                "issue": "",
                "pages": "4171--4186",
                "other_ids": {
                    "DOI": [
                        "10.18653/v1/N19-1423"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.",
                "links": null
            },
            "BIBREF9": {
                "ref_id": "b9",
                "title": "SeVeN: Augmenting word embeddings with unsupervised relation vectors",
                "authors": [
                    {
                        "first": "Luis",
                        "middle": [],
                        "last": "Espinosa-Anke",
                        "suffix": ""
                    },
                    {
                        "first": "Steven",
                        "middle": [],
                        "last": "Schockaert",
                        "suffix": ""
                    }
                ],
                "year": 2018,
                "venue": "Proceedings of the 27th International Conference on Computational Linguistics",
                "volume": "",
                "issue": "",
                "pages": "2653--2665",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Luis Espinosa-Anke and Steven Schockaert. 2018. SeVeN: Augmenting word embeddings with unsu- pervised relation vectors. In Proceedings of the 27th International Conference on Computational Linguis- tics, pages 2653-2665, Santa Fe, New Mexico, USA. Association for Computational Linguistics.",
                "links": null
            },
            "BIBREF10": {
                "ref_id": "b10",
                "title": "Prognostic value of comormidity for severity of covid-19: A systematic review and meta-analysis study",
                "authors": [
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Fathi",
                        "suffix": ""
                    },
                    {
                        "first": "Khatoon",
                        "middle": [],
                        "last": "Vakili",
                        "suffix": ""
                    },
                    {
                        "first": "Fatemeh",
                        "middle": [],
                        "last": "Sayehmiri",
                        "suffix": ""
                    },
                    {
                        "first": "Abdolrahman",
                        "middle": [],
                        "last": "Mohamadkhani",
                        "suffix": ""
                    },
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Hajiesmaeili",
                        "suffix": ""
                    },
                    {
                        "first": "Mostafa",
                        "middle": [],
                        "last": "Rezaei-Tavirani",
                        "suffix": ""
                    },
                    {
                        "first": "Owrang",
                        "middle": [],
                        "last": "Eilami",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "M. Fathi, Khatoon Vakili, Fatemeh Sayehmiri, Abdol- rahman Mohamadkhani, M. Hajiesmaeili, Mostafa Rezaei-Tavirani, and Owrang Eilami. 2020. Prog- nostic value of comormidity for severity of covid- 19: A systematic review and meta-analysis study. In medRxiv.",
                "links": null
            },
            "BIBREF11": {
                "ref_id": "b11",
                "title": "Who is more susceptible to covid-19 infection and mortality in the states? medRxiv",
                "authors": [
                    {
                        "first": "Yang",
                        "middle": [],
                        "last": "Han",
                        "suffix": ""
                    },
                    {
                        "first": "O",
                        "middle": [
                            "K"
                        ],
                        "last": "Victor",
                        "suffix": ""
                    },
                    {
                        "first": "Jacqueline",
                        "middle": [
                            "C K"
                        ],
                        "last": "Li",
                        "suffix": ""
                    },
                    {
                        "first": "Peiyang",
                        "middle": [],
                        "last": "Lam",
                        "suffix": ""
                    },
                    {
                        "first": "Ruiqiao",
                        "middle": [],
                        "last": "Guo",
                        "suffix": ""
                    },
                    {
                        "first": "Wilton",
                        "middle": [
                            "W T"
                        ],
                        "last": "Bai",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Fok",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {
                    "DOI": [
                        "10.1101/2020.05.01.20087403"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Yang Han, Victor O.K. Li, Jacqueline C.K. Lam, Peiyang Guo, Ruiqiao Bai, and Wilton W.T. Fok. 2020. Who is more susceptible to covid-19 infec- tion and mortality in the states? medRxiv.",
                "links": null
            },
            "BIBREF12": {
                "ref_id": "b12",
                "title": "Relative coronavirus disease 2019 mortality: A swiss population-based study",
                "authors": [
                    {
                        "first": "Torsten",
                        "middle": [],
                        "last": "Hothorn",
                        "suffix": ""
                    },
                    {
                        "first": "Marie-Charlotte",
                        "middle": [],
                        "last": "Bopp",
                        "suffix": ""
                    },
                    {
                        "first": "H",
                        "middle": [
                            "F"
                        ],
                        "last": "Guenthard",
                        "suffix": ""
                    },
                    {
                        "first": "Olivia",
                        "middle": [],
                        "last": "Keiser",
                        "suffix": ""
                    },
                    {
                        "first": "Michel",
                        "middle": [],
                        "last": "Roelens",
                        "suffix": ""
                    },
                    {
                        "first": "Caroline",
                        "middle": [
                            "E"
                        ],
                        "last": "Weibull",
                        "suffix": ""
                    },
                    {
                        "first": "Michael",
                        "middle": [
                            "J"
                        ],
                        "last": "Crowther",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Torsten Hothorn, Marie-Charlotte Bopp, H. F. Guen- thard, Olivia Keiser, Michel Roelens, Caroline E Weibull, and Michael J Crowther. 2020. Rela- tive coronavirus disease 2019 mortality: A swiss population-based study. In medRxiv.",
                "links": null
            },
            "BIBREF13": {
                "ref_id": "b13",
                "title": "A probabilistic model of information retrieval: development and comparative experiments -part 1",
                "authors": [
                    {
                        "first": "Karen",
                        "middle": [
                            "Sp\u00e4rck"
                        ],
                        "last": "Jones",
                        "suffix": ""
                    },
                    {
                        "first": "Steve",
                        "middle": [],
                        "last": "Walker",
                        "suffix": ""
                    },
                    {
                        "first": "Stephen",
                        "middle": [
                            "E"
                        ],
                        "last": "Robertson",
                        "suffix": ""
                    }
                ],
                "year": 2000,
                "venue": "Inf. Process. Manag",
                "volume": "36",
                "issue": "",
                "pages": "779--808",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Karen Sp\u00e4rck Jones, Steve Walker, and Stephen E. Robertson. 2000. A probabilistic model of informa- tion retrieval: development and comparative experi- ments -part 1. Inf. Process. Manag., 36:779-808.",
                "links": null
            },
            "BIBREF14": {
                "ref_id": "b14",
                "title": "Multisystem inflammatory syndrome in children (mis-c) associated with sars-cov-2 infection: A multi-institutional study from new york city",
                "authors": [
                    {
                        "first": "Shubhi",
                        "middle": [],
                        "last": "Kaushik",
                        "suffix": ""
                    },
                    {
                        "first": "Scott",
                        "middle": [
                            "I"
                        ],
                        "last": "Aydin",
                        "suffix": ""
                    },
                    {
                        "first": "Kim",
                        "middle": [
                            "R"
                        ],
                        "last": "Derespina",
                        "suffix": ""
                    },
                    {
                        "first": "Prerna",
                        "middle": [],
                        "last": "Bansal",
                        "suffix": ""
                    },
                    {
                        "first": "Shanna",
                        "middle": [],
                        "last": "Kowalsky",
                        "suffix": ""
                    },
                    {
                        "first": "Rebecca",
                        "middle": [],
                        "last": "Trachtman",
                        "suffix": ""
                    },
                    {
                        "first": "Jennifer",
                        "middle": [
                            "K"
                        ],
                        "last": "Gillen",
                        "suffix": ""
                    },
                    {
                        "first": "Michelle",
                        "middle": [
                            "M"
                        ],
                        "last": "Perez",
                        "suffix": ""
                    },
                    {
                        "first": "Sara",
                        "middle": [
                            "H"
                        ],
                        "last": "Soshnick",
                        "suffix": ""
                    },
                    {
                        "first": "Edward",
                        "middle": [
                            "E"
                        ],
                        "last": "Conway",
                        "suffix": ""
                    },
                    {
                        "first": "Asher",
                        "middle": [],
                        "last": "Bercow",
                        "suffix": ""
                    },
                    {
                        "first": "Howard",
                        "middle": [
                            "S"
                        ],
                        "last": "Seiden",
                        "suffix": ""
                    },
                    {
                        "first": "H",
                        "middle": [],
                        "last": "Robert",
                        "suffix": ""
                    },
                    {
                        "first": "Henry",
                        "middle": [
                            "Michael"
                        ],
                        "last": "Pass",
                        "suffix": ""
                    },
                    {
                        "first": "George",
                        "middle": [],
                        "last": "Ushay",
                        "suffix": ""
                    },
                    {
                        "first": "Shivanand S",
                        "middle": [],
                        "last": "Ofori-Amanfo",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Medar",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "The Journal of Pediatrics",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Shubhi Kaushik, Scott I. Aydin, Kim R. Derespina, Pre- rna Bansal, Shanna Kowalsky, Rebecca Trachtman, Jennifer K. Gillen, Michelle M. Perez, Sara H. Sosh- nick, Edward E. Conway, Asher Bercow, Howard S. Seiden, Robert H Pass, Henry Michael Ushay, George Ofori-Amanfo, and Shivanand S Medar. 2020. Multisystem inflammatory syndrome in chil- dren (mis-c) associated with sars-cov-2 infection: A multi-institutional study from new york city. The Journal of Pediatrics.",
                "links": null
            },
            "BIBREF15": {
                "ref_id": "b15",
                "title": "BioBERT: a pre-trained biomedical language representation model for biomedical text mining",
                "authors": [
                    {
                        "first": "Jinhyuk",
                        "middle": [],
                        "last": "Lee",
                        "suffix": ""
                    },
                    {
                        "first": "Wonjin",
                        "middle": [],
                        "last": "Yoon",
                        "suffix": ""
                    },
                    {
                        "first": "Sungdong",
                        "middle": [],
                        "last": "Kim",
                        "suffix": ""
                    },
                    {
                        "first": "Donghyeon",
                        "middle": [],
                        "last": "Kim",
                        "suffix": ""
                    },
                    {
                        "first": "Sunkyu",
                        "middle": [],
                        "last": "Kim",
                        "suffix": ""
                    },
                    {
                        "first": "Chan",
                        "middle": [],
                        "last": "Ho So",
                        "suffix": ""
                    },
                    {
                        "first": "Jaewoo",
                        "middle": [],
                        "last": "Kang",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "Bioinformatics",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics.",
                "links": null
            },
            "BIBREF16": {
                "ref_id": "b16",
                "title": "Identifying radiological findings related to covid-19 from medical literature",
                "authors": [
                    {
                        "first": "Yuxiao",
                        "middle": [],
                        "last": "Liang",
                        "suffix": ""
                    },
                    {
                        "first": "Pengtao",
                        "middle": [],
                        "last": "Xie",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "ArXiv",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Yuxiao Liang and Pengtao Xie. 2020. Identifying ra- diological findings related to covid-19 from medical literature. ArXiv, abs/2004.01862.",
                "links": null
            },
            "BIBREF18": {
                "ref_id": "b18",
                "title": "S2ORC: The Semantic Scholar Open Research Corpus",
                "authors": [
                    {
                        "first": "Kyle",
                        "middle": [],
                        "last": "Lo",
                        "suffix": ""
                    },
                    {
                        "first": "Lucy",
                        "middle": [
                            "Lu"
                        ],
                        "last": "Wang",
                        "suffix": ""
                    },
                    {
                        "first": "Mark",
                        "middle": [],
                        "last": "Neumann",
                        "suffix": ""
                    },
                    {
                        "first": "Rodney",
                        "middle": [],
                        "last": "Kinney",
                        "suffix": ""
                    },
                    {
                        "first": "Daniel",
                        "middle": [
                            "S"
                        ],
                        "last": "Weld",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "Proceedings of ACL",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Kyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kin- ney, and Daniel S. Weld. 2020. S2ORC: The Seman- tic Scholar Open Research Corpus. In Proceedings of ACL.",
                "links": null
            },
            "BIBREF19": {
                "ref_id": "b19",
                "title": "Grobid: Combining automatic bibliographic data recognition and term extraction for scholarship publications",
                "authors": [
                    {
                        "first": "Patrice",
                        "middle": [],
                        "last": "Lopez",
                        "suffix": ""
                    }
                ],
                "year": 2009,
                "venue": "ECDL",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Patrice Lopez. 2009. Grobid: Combining automatic bibliographic data recognition and term extraction for scholarship publications. In ECDL.",
                "links": null
            },
            "BIBREF20": {
                "ref_id": "b20",
                "title": "B\u00e1rbara Padilla-Fern\u00e1ndez, Frank Van der Aa, Salvador Arlandis, and Hashim Hashim. 2020. Management of female and functional urology patients during the covid pandemic",
                "authors": [
                    {
                        "first": "Luis",
                        "middle": [],
                        "last": "L\u00f3pez-Fando",
                        "suffix": ""
                    },
                    {
                        "first": "Paulina",
                        "middle": [],
                        "last": "Bueno",
                        "suffix": ""
                    },
                    {
                        "first": "David",
                        "middle": [],
                        "last": "S\u00e1nchez Carracedo",
                        "suffix": ""
                    },
                    {
                        "first": "Augusto",
                        "middle": [],
                        "last": "M\u00e1rcio",
                        "suffix": ""
                    },
                    {
                        "first": "David",
                        "middle": [
                            "Manuel"
                        ],
                        "last": "Averbeck",
                        "suffix": ""
                    },
                    {
                        "first": "Francisco",
                        "middle": [],
                        "last": "Castro-D\u00edaz",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Cruz",
                        "suffix": ""
                    },
                    {
                        "first": "Enrico",
                        "middle": [],
                        "last": "Roger R Dmochowski",
                        "suffix": ""
                    },
                    {
                        "first": "Sakineh",
                        "middle": [],
                        "last": "Finazzi-Agr\u00f2",
                        "suffix": ""
                    },
                    {
                        "first": "John",
                        "middle": [],
                        "last": "Hajebrahimi",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Heesakkers",
                        "suffix": ""
                    },
                    {
                        "first": "Tufan",
                        "middle": [],
                        "last": "George R Kasyan",
                        "suffix": ""
                    },
                    {
                        "first": "Beno\u00eet",
                        "middle": [],
                        "last": "Tarcan",
                        "suffix": ""
                    },
                    {
                        "first": "Mauricio",
                        "middle": [],
                        "last": "Peyronnet",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Plata",
                        "suffix": ""
                    }
                ],
                "year": null,
                "venue": "European Urology Focus",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Luis L\u00f3pez-Fando, Paulina Bueno, David S\u00e1nchez Car- racedo, M\u00e1rcio Augusto Averbeck, David Manuel Castro-D\u00edaz, emmanuel chartier-kastler, Francisco Cruz, Roger R Dmochowski, Enrico Finazzi-Agr\u00f2, Sakineh Hajebrahimi, John Heesakkers, George R Kasyan, Tufan Tarcan, Beno\u00eet Peyronnet, Mauricio Plata, B\u00e1rbara Padilla-Fern\u00e1ndez, Frank Van der Aa, Salvador Arlandis, and Hashim Hashim. 2020. Man- agement of female and functional urology patients during the covid pandemic. European Urology Fo- cus.",
                "links": null
            },
            "BIBREF21": {
                "ref_id": "b21",
                "title": "Deep relevance ranking using enhanced document-query interactions",
                "authors": [
                    {
                        "first": "Ryan",
                        "middle": [],
                        "last": "Mcdonald",
                        "suffix": ""
                    }
                ],
                "year": 2018,
                "venue": "Georgios-Ioannis Brokos, and Ion Androutsopoulos",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Ryan McDonald, Georgios-Ioannis Brokos, and Ion Androutsopoulos. 2018. Deep relevance ranking using enhanced document-query interactions. In EMNLP.",
                "links": null
            },
            "BIBREF22": {
                "ref_id": "b22",
                "title": "ScispaCy: Fast and robust models for biomedical natural language processing",
                "authors": [
                    {
                        "first": "Mark",
                        "middle": [],
                        "last": "Neumann",
                        "suffix": ""
                    },
                    {
                        "first": "Daniel",
                        "middle": [],
                        "last": "King",
                        "suffix": ""
                    },
                    {
                        "first": "Iz",
                        "middle": [],
                        "last": "Beltagy",
                        "suffix": ""
                    },
                    {
                        "first": "Waleed",
                        "middle": [],
                        "last": "Ammar",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "Proceedings of the 18th BioNLP Workshop and Shared Task",
                "volume": "",
                "issue": "",
                "pages": "319--327",
                "other_ids": {
                    "DOI": [
                        "10.18653/v1/W19-5034"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019. ScispaCy: Fast and robust models for biomedical natural language processing. In Pro- ceedings of the 18th BioNLP Workshop and Shared Task, pages 319-327, Florence, Italy. Association for Computational Linguistics.",
                "links": null
            },
            "BIBREF23": {
                "ref_id": "b23",
                "title": "Prevalence of gastrointestinal symptoms and fecal viral shedding in patients with coronavirus disease 2019",
                "authors": [
                    {
                        "first": "Sravanthi",
                        "middle": [],
                        "last": "Parasa",
                        "suffix": ""
                    },
                    {
                        "first": "Madhav",
                        "middle": [],
                        "last": "Desai",
                        "suffix": ""
                    },
                    {
                        "first": "Harsh",
                        "middle": [],
                        "last": "Viveksandeep Thoguluva Chandrasekar",
                        "suffix": ""
                    },
                    {
                        "first": "Kevin",
                        "middle": [],
                        "last": "Patel",
                        "suffix": ""
                    },
                    {
                        "first": "Thomas",
                        "middle": [],
                        "last": "Kennedy",
                        "suffix": ""
                    },
                    {
                        "first": "Marco",
                        "middle": [],
                        "last": "R\u00f6sch",
                        "suffix": ""
                    },
                    {
                        "first": "Matteo",
                        "middle": [],
                        "last": "Spadaccini",
                        "suffix": ""
                    },
                    {
                        "first": "Roberto",
                        "middle": [],
                        "last": "Colombo",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Gabbiadini",
                        "suffix": ""
                    },
                    {
                        "first": "L",
                        "middle": [
                            "A"
                        ],
                        "last": "Everson",
                        "suffix": ""
                    },
                    {
                        "first": "Alessandro",
                        "middle": [],
                        "last": "Artifon",
                        "suffix": ""
                    },
                    {
                        "first": "Prateek",
                        "middle": [],
                        "last": "Repici",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Sharma",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "JAMA Network Open",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Sravanthi Parasa, Madhav Desai, Viveksandeep Thogu- luva Chandrasekar, Harsh Patel, Kevin Kennedy, Thomas R\u00f6sch, Marco Spadaccini, Matteo Colombo, Roberto Gabbiadini, Everson L. A. Artifon, Alessandro Repici, and Prateek Sharma. 2020. Prevalence of gastrointestinal symptoms and fecal viral shedding in patients with coronavirus disease 2019. JAMA Network Open, 3.",
                "links": null
            },
            "BIBREF24": {
                "ref_id": "b24",
                "title": "Covid-19 and inflammatory bowel diseases: risk assessment, shared molecular pathways and therapeutic challenges",
                "authors": [
                    {
                        "first": "Mircea",
                        "middle": [],
                        "last": "Iolanda Valentina Popa",
                        "suffix": ""
                    },
                    {
                        "first": "Catalina",
                        "middle": [],
                        "last": "Diculescu",
                        "suffix": ""
                    },
                    {
                        "first": "Cristina",
                        "middle": [],
                        "last": "Mihai",
                        "suffix": ""
                    },
                    {
                        "first": "Alexandru",
                        "middle": [],
                        "last": "Cijevschi-Prelipcean",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Burlacu",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {
                    "DOI": [
                        "10.1101/2020.04.28.20082859"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Iolanda Valentina Popa, Mircea Diculescu, Catalina Mihai, Cristina Cijevschi-Prelipcean, and Alexandru Burlacu. 2020. Covid-19 and inflammatory bowel diseases: risk assessment, shared molecular path- ways and therapeutic challenges. medRxiv.",
                "links": null
            },
            "BIBREF25": {
                "ref_id": "b25",
                "title": "Exploring the limits of transfer learning with a unified text-to",
                "authors": [
                    {
                        "first": "Colin",
                        "middle": [],
                        "last": "Raffel",
                        "suffix": ""
                    },
                    {
                        "first": "Noam",
                        "middle": [],
                        "last": "Shazeer",
                        "suffix": ""
                    },
                    {
                        "first": "Adam",
                        "middle": [],
                        "last": "Roberts",
                        "suffix": ""
                    },
                    {
                        "first": "Katherine",
                        "middle": [],
                        "last": "Lee",
                        "suffix": ""
                    },
                    {
                        "first": "Sharan",
                        "middle": [],
                        "last": "Narang",
                        "suffix": ""
                    },
                    {
                        "first": "Michael",
                        "middle": [],
                        "last": "Matena",
                        "suffix": ""
                    },
                    {
                        "first": "Yanqi",
                        "middle": [],
                        "last": "Zhou",
                        "suffix": ""
                    },
                    {
                        "first": "Wei",
                        "middle": [],
                        "last": "Li",
                        "suffix": ""
                    },
                    {
                        "first": "Peter",
                        "middle": [
                            "J"
                        ],
                        "last": "Liu",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text trans- former.",
                "links": null
            },
            "BIBREF26": {
                "ref_id": "b26",
                "title": "Sentence-BERT: Sentence embeddings using Siamese BERTnetworks",
                "authors": [
                    {
                        "first": "Nils",
                        "middle": [],
                        "last": "Reimers",
                        "suffix": ""
                    },
                    {
                        "first": "Iryna",
                        "middle": [],
                        "last": "Gurevych",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
                "volume": "",
                "issue": "",
                "pages": "3982--3992",
                "other_ids": {
                    "DOI": [
                        "10.18653/v1/D19-1410"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Nils Reimers and Iryna Gurevych. 2019. Sentence- BERT: Sentence embeddings using Siamese BERT- networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natu- ral Language Processing (EMNLP-IJCNLP), pages 3982-3992, Hong Kong, China. Association for Computational Linguistics.",
                "links": null
            },
            "BIBREF27": {
                "ref_id": "b27",
                "title": "TREC-COVID: Rationale and Structure of an Information Retrieval Shared Task for COVID-19",
                "authors": [
                    {
                        "first": "Kirk",
                        "middle": [],
                        "last": "Roberts",
                        "suffix": ""
                    },
                    {
                        "first": "Tasmeer",
                        "middle": [],
                        "last": "Alam",
                        "suffix": ""
                    },
                    {
                        "first": "Steven",
                        "middle": [],
                        "last": "Bedrick",
                        "suffix": ""
                    },
                    {
                        "first": "Dina",
                        "middle": [],
                        "last": "Demner-Fushman",
                        "suffix": ""
                    },
                    {
                        "first": "Kyle",
                        "middle": [],
                        "last": "Lo",
                        "suffix": ""
                    },
                    {
                        "first": "Ian",
                        "middle": [],
                        "last": "Soboroff",
                        "suffix": ""
                    },
                    {
                        "first": "Ellen",
                        "middle": [],
                        "last": "Voorhees",
                        "suffix": ""
                    },
                    {
                        "first": "Lucy",
                        "middle": [
                            "Lu"
                        ],
                        "last": "Wang",
                        "suffix": ""
                    },
                    {
                        "first": "William R",
                        "middle": [],
                        "last": "Hersh",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "Journal of the American Medical Informatics Association. Ocaa091",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {
                    "DOI": [
                        "10.1093/jamia/ocaa091"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Kirk Roberts, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, Kyle Lo, Ian Soboroff, Ellen Voorhees, Lucy Lu Wang, and William R Hersh. 2020. TREC-COVID: Rationale and Structure of an Information Retrieval Shared Task for COVID- 19. Journal of the American Medical Informatics Association. Ocaa091.",
                "links": null
            },
            "BIBREF28": {
                "ref_id": "b28",
                "title": "Exploring the sars-cov-2 virus-host-drug interactome for drug repurposing",
                "authors": [
                    {
                        "first": "Sepideh",
                        "middle": [],
                        "last": "Sadegh",
                        "suffix": ""
                    },
                    {
                        "first": "Julian",
                        "middle": [],
                        "last": "Matschinske",
                        "suffix": ""
                    },
                    {
                        "first": "David",
                        "middle": [
                            "B"
                        ],
                        "last": "Blumenthal",
                        "suffix": ""
                    },
                    {
                        "first": "Gihanna",
                        "middle": [],
                        "last": "Galindez",
                        "suffix": ""
                    },
                    {
                        "first": "Tim",
                        "middle": [],
                        "last": "Kacprowski",
                        "suffix": ""
                    },
                    {
                        "first": "Markus",
                        "middle": [],
                        "last": "List",
                        "suffix": ""
                    },
                    {
                        "first": "Reza",
                        "middle": [],
                        "last": "Nasirigerdeh",
                        "suffix": ""
                    },
                    {
                        "first": "Mhaned",
                        "middle": [],
                        "last": "Oubounyt",
                        "suffix": ""
                    },
                    {
                        "first": "Andreas",
                        "middle": [],
                        "last": "Pichlmair",
                        "suffix": ""
                    },
                    {
                        "first": "Tim",
                        "middle": [
                            "Daniel"
                        ],
                        "last": "Rose",
                        "suffix": ""
                    },
                    {
                        "first": "Marisol",
                        "middle": [],
                        "last": "Salgado-Albarr\u00e1n",
                        "suffix": ""
                    },
                    {
                        "first": "Julian",
                        "middle": [],
                        "last": "Sp\u00e4th",
                        "suffix": ""
                    },
                    {
                        "first": "Alexey",
                        "middle": [],
                        "last": "Stukalov",
                        "suffix": ""
                    },
                    {
                        "first": "Nina",
                        "middle": [
                            "K"
                        ],
                        "last": "Wenke",
                        "suffix": ""
                    },
                    {
                        "first": "Kevin",
                        "middle": [],
                        "last": "Yuan",
                        "suffix": ""
                    },
                    {
                        "first": "Josch",
                        "middle": [
                            "K"
                        ],
                        "last": "Pauling",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Sepideh Sadegh, Julian Matschinske, David B. Blu- menthal, Gihanna Galindez, Tim Kacprowski, Markus List, Reza Nasirigerdeh, Mhaned Oubounyt, Andreas Pichlmair, Tim Daniel Rose, Marisol Salgado-Albarr\u00e1n, Julian Sp\u00e4th, Alexey Stukalov, Nina K. Wenke, Kevin Yuan, Josch K. Pauling, and Jan Baumbach. 2020. Exploring the sars-cov-2 virus-host-drug interactome for drug repurposing.",
                "links": null
            },
            "BIBREF29": {
                "ref_id": "b29",
                "title": "Real-time open-domain question answering with dense-sparse phrase index",
                "authors": [
                    {
                        "first": "Minjoon",
                        "middle": [],
                        "last": "Seo",
                        "suffix": ""
                    },
                    {
                        "first": "Jinhyuk",
                        "middle": [],
                        "last": "Lee",
                        "suffix": ""
                    },
                    {
                        "first": "Tom",
                        "middle": [],
                        "last": "Kwiatkowski",
                        "suffix": ""
                    },
                    {
                        "first": "Ankur",
                        "middle": [],
                        "last": "Parikh",
                        "suffix": ""
                    },
                    {
                        "first": "Ali",
                        "middle": [],
                        "last": "Farhadi",
                        "suffix": ""
                    },
                    {
                        "first": "Hannaneh",
                        "middle": [],
                        "last": "Hajishirzi",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
                "volume": "",
                "issue": "",
                "pages": "4430--4441",
                "other_ids": {
                    "DOI": [
                        "10.18653/v1/P19-1436"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur Parikh, Ali Farhadi, and Hannaneh Hajishirzi. 2019. Real-time open-domain question answering with dense-sparse phrase index. In Proceedings of the 57th Annual Meeting of the Association for Com- putational Linguistics, pages 4430-4441, Florence, Italy. Association for Computational Linguistics.",
                "links": null
            },
            "BIBREF30": {
                "ref_id": "b30",
                "title": "A web-scale system for scientific knowledge exploration",
                "authors": [
                    {
                        "first": "Zhihong",
                        "middle": [],
                        "last": "Shen",
                        "suffix": ""
                    },
                    {
                        "first": "Hao",
                        "middle": [],
                        "last": "Ma",
                        "suffix": ""
                    },
                    {
                        "first": "Kuansan",
                        "middle": [],
                        "last": "Wang",
                        "suffix": ""
                    }
                ],
                "year": 2018,
                "venue": "Proceedings of ACL 2018, System Demonstrations",
                "volume": "",
                "issue": "",
                "pages": "87--92",
                "other_ids": {
                    "DOI": [
                        "10.18653/v1/P18-4015"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Zhihong Shen, Hao Ma, and Kuansan Wang. 2018. A web-scale system for scientific knowledge explo- ration. In Proceedings of ACL 2018, System Demon- strations, pages 87-92, Melbourne, Australia. Asso- ciation for Computational Linguistics.",
                "links": null
            },
            "BIBREF31": {
                "ref_id": "b31",
                "title": "Seroprevalence of anti-sars-cov-2 igg antibodies in geneva, switzerland (serocov-pop): a populationbased study",
                "authors": [
                    {
                        "first": "Silvia",
                        "middle": [],
                        "last": "Stringhini",
                        "suffix": ""
                    },
                    {
                        "first": "Ania",
                        "middle": [],
                        "last": "Wisniak",
                        "suffix": ""
                    },
                    {
                        "first": "Giovanni",
                        "middle": [],
                        "last": "Piumatti",
                        "suffix": ""
                    },
                    {
                        "first": "Andrew",
                        "middle": [
                            "S"
                        ],
                        "last": "Azman",
                        "suffix": ""
                    },
                    {
                        "first": "A",
                        "middle": [],
                        "last": "Stephen",
                        "suffix": ""
                    },
                    {
                        "first": "H\u00e9l\u00e8ne",
                        "middle": [],
                        "last": "Lauer",
                        "suffix": ""
                    },
                    {
                        "first": "David",
                        "middle": [],
                        "last": "Baysson",
                        "suffix": ""
                    },
                    {
                        "first": "Dusan",
                        "middle": [],
                        "last": "De Ridder",
                        "suffix": ""
                    },
                    {
                        "first": "Stephanie",
                        "middle": [],
                        "last": "Petrovic",
                        "suffix": ""
                    },
                    {
                        "first": "Kailing",
                        "middle": [],
                        "last": "Schrempft",
                        "suffix": ""
                    },
                    {
                        "first": "Sabine",
                        "middle": [],
                        "last": "Marcus",
                        "suffix": ""
                    },
                    {
                        "first": "Isabelle",
                        "middle": [
                            "Arm"
                        ],
                        "last": "Yerly",
                        "suffix": ""
                    },
                    {
                        "first": "Olivia",
                        "middle": [],
                        "last": "Vernez",
                        "suffix": ""
                    },
                    {
                        "first": "Samia",
                        "middle": [],
                        "last": "Keiser",
                        "suffix": ""
                    },
                    {
                        "first": "Klara",
                        "middle": [
                            "M"
                        ],
                        "last": "Hurst",
                        "suffix": ""
                    },
                    {
                        "first": "Didier",
                        "middle": [],
                        "last": "Posfay-Barbe",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Trono",
                        "suffix": ""
                    }
                ],
                "year": null,
                "venue": "Lancet",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Silvia Stringhini, Ania Wisniak, Giovanni Piumatti, Andrew S. Azman, Stephen A Lauer, H\u00e9l\u00e8ne Baysson, David De Ridder, Dusan Petrovic, Stephanie Schrempft, Kailing Marcus, Sabine Yerly, Isabelle Arm Vernez, Olivia Keiser, Samia Hurst, Klara M Posfay-Barbe, Didier Trono, Didier Pit- tet, Laurent G\u00e9taz, Fran\u00e7ois Chappuis, Isabella Eck- erle, Nicolas Vuilleumier, Benjamin Meyer, Antoine Flahault, Laurent Kaiser, and Idris Guessous. 2020. Seroprevalence of anti-sars-cov-2 igg antibodies in geneva, switzerland (serocov-pop): a population- based study. Lancet (London, England).",
                "links": null
            },
            "BIBREF32": {
                "ref_id": "b32",
                "title": "\u00c9ric Gaussier, Liliana Barrio-Alvers, Michael Schroeder, Ion Androutsopoulos, and Georgios Paliouras. 2015. An overview of the bioasq large-scale biomedical semantic indexing and question answering competition",
                "authors": [
                    {
                        "first": "George",
                        "middle": [],
                        "last": "Tsatsaronis",
                        "suffix": ""
                    },
                    {
                        "first": "Georgios",
                        "middle": [],
                        "last": "Balikas",
                        "suffix": ""
                    },
                    {
                        "first": "Prodromos",
                        "middle": [],
                        "last": "Malakasiotis",
                        "suffix": ""
                    },
                    {
                        "first": "Ioannis",
                        "middle": [],
                        "last": "Partalas",
                        "suffix": ""
                    },
                    {
                        "first": "Matthias",
                        "middle": [],
                        "last": "Zschunke",
                        "suffix": ""
                    },
                    {
                        "first": "Michael",
                        "middle": [
                            "R"
                        ],
                        "last": "Alvers",
                        "suffix": ""
                    },
                    {
                        "first": "Dirk",
                        "middle": [],
                        "last": "Weissenborn",
                        "suffix": ""
                    },
                    {
                        "first": "Anastasia",
                        "middle": [],
                        "last": "Krithara",
                        "suffix": ""
                    }
                ],
                "year": null,
                "venue": "Sergios Petridis, Dimitris Polychronopoulos, Yannis Almirantis, John Pavlopoulos, Nicolas Baskiotis, Patrick Gallinari, Thierry Arti\u00e8res, Axel-Cyrille Ngonga Ngomo",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "George Tsatsaronis, Georgios Balikas, Prodromos Malakasiotis, Ioannis Partalas, Matthias Zschunke, Michael R. Alvers, Dirk Weissenborn, Anastasia Krithara, Sergios Petridis, Dimitris Polychronopou- los, Yannis Almirantis, John Pavlopoulos, Nico- las Baskiotis, Patrick Gallinari, Thierry Arti\u00e8res, Axel-Cyrille Ngonga Ngomo, Norman Heino,\u00c9ric Gaussier, Liliana Barrio-Alvers, Michael Schroeder, Ion Androutsopoulos, and Georgios Paliouras. 2015. An overview of the bioasq large-scale biomedical semantic indexing and question answering competi- tion. In BMC Bioinformatics.",
                "links": null
            },
            "BIBREF33": {
                "ref_id": "b33",
                "title": "TREC-COVID: Constructing a pandemic information retrieval test collection. SIGIR Forum",
                "authors": [
                    {
                        "first": "Ellen",
                        "middle": [],
                        "last": "Voorhees",
                        "suffix": ""
                    },
                    {
                        "first": "Tasmeer",
                        "middle": [],
                        "last": "Alam",
                        "suffix": ""
                    },
                    {
                        "first": "Steven",
                        "middle": [],
                        "last": "Bedrick",
                        "suffix": ""
                    },
                    {
                        "first": "Dina",
                        "middle": [],
                        "last": "Demner-Fushman",
                        "suffix": ""
                    },
                    {
                        "first": "R",
                        "middle": [],
                        "last": "William",
                        "suffix": ""
                    },
                    {
                        "first": "Kyle",
                        "middle": [],
                        "last": "Hersh",
                        "suffix": ""
                    },
                    {
                        "first": "Kirk",
                        "middle": [],
                        "last": "Lo",
                        "suffix": ""
                    },
                    {
                        "first": "Ian",
                        "middle": [],
                        "last": "Roberts",
                        "suffix": ""
                    },
                    {
                        "first": "Lucy",
                        "middle": [
                            "Lu"
                        ],
                        "last": "Soboroff",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Wang",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2020. TREC-COVID: Constructing a pandemic informa- tion retrieval test collection. SIGIR Forum, 54.",
                "links": null
            },
            "BIBREF34": {
                "ref_id": "b34",
                "title": "Microsoft academic graph: When experts are not enough",
                "authors": [
                    {
                        "first": "Kuansan",
                        "middle": [],
                        "last": "Wang",
                        "suffix": ""
                    },
                    {
                        "first": "Zhihong",
                        "middle": [],
                        "last": "Shen",
                        "suffix": ""
                    },
                    {
                        "first": "Chiyuan",
                        "middle": [],
                        "last": "Huang",
                        "suffix": ""
                    },
                    {
                        "first": "Chieh-Han",
                        "middle": [],
                        "last": "Wu",
                        "suffix": ""
                    },
                    {
                        "first": "Yuxiao",
                        "middle": [],
                        "last": "Dong",
                        "suffix": ""
                    },
                    {
                        "first": "Anshul",
                        "middle": [],
                        "last": "Kanakia",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "Quantitative Science Studies",
                "volume": "1",
                "issue": "1",
                "pages": "396--413",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh- Han Wu, Yuxiao Dong, and Anshul Kanakia. 2020. Microsoft academic graph: When experts are not enough. Quantitative Science Studies, 1(1):396- 413.",
                "links": null
            },
            "BIBREF35": {
                "ref_id": "b35",
                "title": "A review of microsoft academic services for science of science studies",
                "authors": [
                    {
                        "first": "Kuansan",
                        "middle": [],
                        "last": "Wang",
                        "suffix": ""
                    },
                    {
                        "first": "Zhihong",
                        "middle": [],
                        "last": "Shen",
                        "suffix": ""
                    },
                    {
                        "first": "Chiyuan",
                        "middle": [],
                        "last": "Huang",
                        "suffix": ""
                    },
                    {
                        "first": "Chieh-Han",
                        "middle": [],
                        "last": "Wu",
                        "suffix": ""
                    },
                    {
                        "first": "Darrin",
                        "middle": [],
                        "last": "Eide",
                        "suffix": ""
                    },
                    {
                        "first": "Yuxiao",
                        "middle": [],
                        "last": "Dong",
                        "suffix": ""
                    },
                    {
                        "first": "Junjie",
                        "middle": [],
                        "last": "Qian",
                        "suffix": ""
                    },
                    {
                        "first": "Anshul",
                        "middle": [],
                        "last": "Kanakia",
                        "suffix": ""
                    },
                    {
                        "first": "Alvin",
                        "middle": [],
                        "last": "Chen",
                        "suffix": ""
                    },
                    {
                        "first": "Richard",
                        "middle": [],
                        "last": "Rogahn",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh- Han Wu, Darrin Eide, Yuxiao Dong, Junjie Qian, Anshul Kanakia, Alvin Chen, and Richard Rogahn. 2019. A review of microsoft academic services for science of science studies. Frontiers in Big Data, 2.",
                "links": null
            },
            "BIBREF36": {
                "ref_id": "b36",
                "title": "Comprehensive named entity recognition on cord-19 with distant or weak supervision. ArXiv, abs",
                "authors": [
                    {
                        "first": "Xuan",
                        "middle": [],
                        "last": "Wang",
                        "suffix": ""
                    },
                    {
                        "first": "Xiangchen",
                        "middle": [],
                        "last": "Song",
                        "suffix": ""
                    },
                    {
                        "first": "Yingjun",
                        "middle": [],
                        "last": "Guan",
                        "suffix": ""
                    },
                    {
                        "first": "Bangzheng",
                        "middle": [],
                        "last": "Li",
                        "suffix": ""
                    },
                    {
                        "first": "Jiawei",
                        "middle": [],
                        "last": "Han",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Xuan Wang, Xiangchen Song, Yingjun Guan, Bangzheng Li, and Jiawei Han. 2020. Compre- hensive named entity recognition on cord-19 with distant or weak supervision. ArXiv, abs/2003.12218.",
                "links": null
            },
            "BIBREF37": {
                "ref_id": "b37",
                "title": "Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature",
                "authors": [
                    {
                        "first": "Leigh",
                        "middle": [],
                        "last": "Weston",
                        "suffix": ""
                    },
                    {
                        "first": "Vahe",
                        "middle": [],
                        "last": "Tshitoyan",
                        "suffix": ""
                    },
                    {
                        "first": "John",
                        "middle": [],
                        "last": "Dagdelen",
                        "suffix": ""
                    },
                    {
                        "first": "Olga",
                        "middle": [],
                        "last": "Kononova",
                        "suffix": ""
                    },
                    {
                        "first": "Kristin",
                        "middle": [],
                        "last": "Persson",
                        "suffix": ""
                    },
                    {
                        "first": "Gerbrand",
                        "middle": [],
                        "last": "Ceder",
                        "suffix": ""
                    },
                    {
                        "first": "Anubhav",
                        "middle": [],
                        "last": "Jain",
                        "suffix": ""
                    }
                ],
                "year": 2019,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {
                    "DOI": [
                        "10.26434/chemrxiv.8226068.v1"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Leigh Weston, Vahe Tshitoyan, John Dagdelen, Olga Kononova, Kristin Persson, Gerbrand Ceder, and Anubhav Jain. 2019. Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature.",
                "links": null
            },
            "BIBREF38": {
                "ref_id": "b38",
                "title": "Safe management of bodies of deceased persons with suspected or confirmed covid-19: a rapid systematic review",
                "authors": [
                    {
                        "first": "Sally",
                        "middle": [],
                        "last": "Yaacoub",
                        "suffix": ""
                    },
                    {
                        "first": "J",
                        "middle": [],
                        "last": "Holger",
                        "suffix": ""
                    },
                    {
                        "first": "Joanne",
                        "middle": [],
                        "last": "Sch\u00fcnemann",
                        "suffix": ""
                    },
                    {
                        "first": "Amena",
                        "middle": [],
                        "last": "Khabsa",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "El-Harakeh",
                        "suffix": ""
                    },
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Assem",
                        "suffix": ""
                    },
                    {
                        "first": "Fatimah",
                        "middle": [],
                        "last": "Khamis",
                        "suffix": ""
                    },
                    {
                        "first": "Rayane",
                        "middle": [
                            "El"
                        ],
                        "last": "Chamseddine",
                        "suffix": ""
                    },
                    {
                        "first": "Zahra",
                        "middle": [],
                        "last": "Khoury",
                        "suffix": ""
                    },
                    {
                        "first": "Layal",
                        "middle": [],
                        "last": "Saad",
                        "suffix": ""
                    },
                    {
                        "first": "Carlos",
                        "middle": [
                            "Cuello"
                        ],
                        "last": "Hneiny",
                        "suffix": ""
                    },
                    {
                        "first": "Giovanna",
                        "middle": [
                            "Elsa"
                        ],
                        "last": "Garcia",
                        "suffix": ""
                    },
                    {
                        "first": "Ute",
                        "middle": [],
                        "last": "Muti-Sch\u00fcnemann",
                        "suffix": ""
                    },
                    {
                        "first": "Antonio",
                        "middle": [],
                        "last": "Bognanni",
                        "suffix": ""
                    },
                    {
                        "first": "Chen",
                        "middle": [],
                        "last": "Chen",
                        "suffix": ""
                    },
                    {
                        "first": "Guang",
                        "middle": [],
                        "last": "Chen",
                        "suffix": ""
                    },
                    {
                        "first": "Yuan",
                        "middle": [],
                        "last": "Zhang",
                        "suffix": ""
                    },
                    {
                        "first": "Hong",
                        "middle": [],
                        "last": "Zhao",
                        "suffix": ""
                    },
                    {
                        "first": "Pierre",
                        "middle": [
                            "Abi"
                        ],
                        "last": "Hanna",
                        "suffix": ""
                    },
                    {
                        "first": "Mark",
                        "middle": [],
                        "last": "Loeb",
                        "suffix": ""
                    },
                    {
                        "first": "Thomas",
                        "middle": [],
                        "last": "Piggott",
                        "suffix": ""
                    },
                    {
                        "first": "Marge",
                        "middle": [],
                        "last": "Reinap",
                        "suffix": ""
                    },
                    {
                        "first": "Nesrine",
                        "middle": [],
                        "last": "Rizk",
                        "suffix": ""
                    },
                    {
                        "first": "Rosa",
                        "middle": [],
                        "last": "Stalteri",
                        "suffix": ""
                    },
                    {
                        "first": "Stephanie",
                        "middle": [],
                        "last": "Duda",
                        "suffix": ""
                    },
                    {
                        "first": "Karla",
                        "middle": [],
                        "last": "Solo",
                        "suffix": ""
                    },
                    {
                        "first": "K",
                        "middle": [],
                        "last": "Derek",
                        "suffix": ""
                    },
                    {
                        "first": "Elie",
                        "middle": [
                            "A"
                        ],
                        "last": "Chu",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Akl",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "BMJ Global Health",
                "volume": "5",
                "issue": "5",
                "pages": "",
                "other_ids": {
                    "DOI": [
                        "10.1136/bmjgh-2020-002650"
                    ]
                },
                "num": null,
                "urls": [],
                "raw_text": "Sally Yaacoub, Holger J Sch\u00fcnemann, Joanne Khabsa, Amena El-Harakeh, Assem M Khamis, Fatimah Chamseddine, Rayane El Khoury, Zahra Saad, Layal Hneiny, Carlos Cuello Garcia, Giovanna Elsa Ute Muti-Sch\u00fcnemann, Antonio Bognanni, Chen Chen, Guang Chen, Yuan Zhang, Hong Zhao, Pierre Abi Hanna, Mark Loeb, Thomas Piggott, Marge Reinap, Nesrine Rizk, Rosa Stalteri, Stephanie Duda, Karla Solo, Derek K Chu, and Elie A Akl. 2020. Safe management of bodies of deceased per- sons with suspected or confirmed covid-19: a rapid systematic review. BMJ Global Health, 5(5).",
                "links": null
            },
            "BIBREF39": {
                "ref_id": "b39",
                "title": "Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context",
                "authors": [
                    {
                        "first": "Xinyi",
                        "middle": [],
                        "last": "Zheng",
                        "suffix": ""
                    },
                    {
                        "first": "Doug",
                        "middle": [],
                        "last": "Burdick",
                        "suffix": ""
                    },
                    {
                        "first": "Lucian",
                        "middle": [],
                        "last": "Popa",
                        "suffix": ""
                    },
                    {
                        "first": "Xin Ru Nancy",
                        "middle": [],
                        "last": "Wang",
                        "suffix": ""
                    }
                ],
                "year": 2020,
                "venue": "ArXiv",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Xinyi Zheng, Doug Burdick, Lucian Popa, and Xin Ru Nancy Wang. 2020. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. ArXiv, abs/2005.00589.",
                "links": null
            }
        },
        "ref_entries": {
            "FIGREF0": {
                "type_str": "figure",
                "uris": null,
                "text": "Papers and preprints are collected from different sources through Semantic Scholar. Released as part of CORD-19 are the harmonized and deduplicated metadata and full text JSON.",
                "num": null
            },
            "FIGREF1": {
                "type_str": "figure",
                "uris": null,
                "text": "The distribution of papers per year in CORD-19. A spike in publications occurs in 2020 in response to COVID-19.",
                "num": null
            },
            "TABREF0": {
                "content": "<table/>",
                "text": "Table understanding (also part of Watson Discovery) then annotates the extracted tables with additional semantic information, such as column and row headers and table captions. We leverage the Global Table Extractor (GTE)(Zheng et al.,",
                "html": null,
                "num": null,
                "type_str": "table"
            },
            "TABREF1": {
                "content": "<table><tr><td>Subfield</td><td>Count</td><td>% of corpus</td></tr><tr><td>Virology</td><td>29567</td><td>25.5%</td></tr><tr><td>Immunology</td><td>15954</td><td>13.8%</td></tr><tr><td>Surgery</td><td>15667</td><td>13.5%</td></tr><tr><td>Internal medicine</td><td>12045</td><td>10.4%</td></tr><tr><td>Intensive care medicine</td><td>10624</td><td>9.2%</td></tr><tr><td>Molecular biology</td><td>7268</td><td>6.3%</td></tr><tr><td>Pathology</td><td>6611</td><td>5.7%</td></tr><tr><td>Genetics</td><td>5231</td><td>4.5%</td></tr><tr><td>Other</td><td>12997</td><td>11.2%</td></tr></table>",
                "text": "A breakdown of the most common MAG18  MAG identifier mappings are provided as a supplement",
                "html": null,
                "num": null,
                "type_str": "table"
            },
            "TABREF2": {
                "content": "<table/>",
                "text": "MAG subfield of study for CORD-19 papers.",
                "html": null,
                "num": null,
                "type_str": "table"
            },
            "TABREF4": {
                "content": "<table><tr><td>changing information needs, the shared task is or-</td></tr><tr><td>ganized in multiple rounds. Each round uses a spe-</td></tr><tr><td>cific version of CORD-19, has newly added topics,</td></tr><tr><td>and gives participants one week to submit per-topic</td></tr><tr><td>document rankings for judgment. Round 1 topics</td></tr><tr><td>included more general questions such as What is</td></tr><tr><td>the origin of COVID-19? and What are the initial</td></tr><tr><td>symptoms of COVID-19? while Round 3 topics</td></tr><tr><td>have become more focused, e.g., What are the ob-</td></tr><tr><td>served mutations in the SARS-CoV-2 genome? and</td></tr><tr><td>What are the longer-term complications of those</td></tr><tr><td>who recover from COVID-19? Around 60 medi-</td></tr><tr><td>cal domain experts, including indexers from NLM</td></tr><tr><td>and medical students from OHSU and UTHealth,</td></tr><tr><td>are involved in providing gold rankings for evalu-</td></tr><tr><td>ation. TREC-COVID opened using the April 1st</td></tr><tr><td>CORD-19 version and received submissions from</td></tr><tr><td>over 55 participating teams.</td></tr></table>",
                "text": "Publicly-available tools and systems for medical experts using",
                "html": null,
                "num": null,
                "type_str": "table"
            }
        }
    }
}