| --- |
| annotations_creators: [] |
| language: |
| - en |
| language_creators: |
| - found |
| license: |
| - mit |
| multilinguality: |
| - monolingual |
| paperswithcode_id: acronym-identification |
| pretty_name: acl-ocl-corpus |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - original |
| tags: |
| - research papers |
| - acl |
| task_categories: |
| - token-classification |
| task_ids: [] |
| train-eval-index: |
| - col_mapping: |
| labels: tags |
| tokens: tokens |
| config: default |
| splits: |
| eval_split: test |
| task: token-classification |
| task_id: entity_extraction |
| --- |
| |
| # Dataset Card for ACL Anthology Corpus |
|
|
| [](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
|
|
| This repository provides full-text and metadata to the ACL anthology collection (80k articles/posters as of September 2022) also including .pdf files and grobid extractions of the pdfs. |
|
|
| ## How is this different from what ACL anthology provides and what already exists? |
|
|
| - We provide pdfs, full-text, references and other details extracted by grobid from the PDFs while [ACL Anthology](https://aclanthology.org/anthology+abstracts.bib.gz) only provides abstracts. |
| - There exists a similar corpus call [ACL Anthology Network](https://clair.eecs.umich.edu/aan/about.php) but is now showing its age with just 23k papers from Dec 2016. |
|
|
|
|
| ```python |
| >>> import pandas as pd |
| >>> df = pd.read_parquet('acl-publication-info.74k.parquet') |
| >>> df |
| acl_id abstract full_text corpus_paper_id pdf_hash ... number volume journal editor isbn |
| 0 O02-2002 There is a need to measure word similarity whe... There is a need to measure word similarity whe... 18022704 0b09178ac8d17a92f16140365363d8df88c757d0 ... None None None None None |
| 1 L02-1310 8220988 8d5e31610bc82c2abc86bc20ceba684c97e66024 ... None None None None None |
| 2 R13-1042 Thread disentanglement is the task of separati... Thread disentanglement is the task of separati... 16703040 3eb736b17a5acb583b9a9bd99837427753632cdb ... None None None None None |
| 3 W05-0819 In this paper, we describe a word alignment al... In this paper, we describe a word alignment al... 1215281 b20450f67116e59d1348fc472cfc09f96e348f55 ... None None None None None |
| 4 L02-1309 18078432 011e943b64a78dadc3440674419821ee080f0de3 ... None None None None None |
| ... ... ... ... ... ... ... ... ... ... ... ... |
| 73280 P99-1002 This paper describes recent progress and the a... This paper describes recent progress and the a... 715160 ab17a01f142124744c6ae425f8a23011366ec3ee ... None None None None None |
| 73281 P00-1009 We present an LFG-DOP parser which uses fragme... We present an LFG-DOP parser which uses fragme... 1356246 ad005b3fd0c867667118482227e31d9378229751 ... None None None None None |
| 73282 P99-1056 The processes through which readers evoke ment... The processes through which readers evoke ment... 7277828 924cf7a4836ebfc20ee094c30e61b949be049fb6 ... None None None None None |
| 73283 P99-1051 This paper examines the extent to which verb d... This paper examines the extent to which verb d... 1829043 6b1f6f28ee36de69e8afac39461ee1158cd4d49a ... None None None None None |
| 73284 P00-1013 Spoken dialogue managers have benefited from u... Spoken dialogue managers have benefited from u... 10903652 483c818c09e39d9da47103fbf2da8aaa7acacf01 ... None None None None None |
| |
| [73285 rows x 21 columns] |
| ``` |
|
|
|
|
|
|
| ## Table of Contents |
|
|
| - [Table of Contents](#table-of-contents) |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Dataset Creation](#dataset-creation) |
| - [Source Data](#source-data) |
| - [Additional Information](#additional-information) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
|
|
| ## Dataset Description |
|
|
| - **Repository:** https://github.com/shauryr/ACL-anthology-corpus |
| - **Point of Contact:** shauryr@gmail.com |
|
|
| ### Dataset Summary |
|
|
| Dataframe with extracted metadata (table below with details) and full text of the collection for analysis : **size 489M** |
|
|
| ### Languages |
|
|
| en, zh and others |
|
|
| ## Dataset Structure |
|
|
| Dataframe |
|
|
| ### Data Instances |
|
|
| Each row is a paper from ACL anthology |
|
|
| ### Data Fields |
|
|
| | **Column name** | **Description** | |
| | :---------------: | :---------------------------: | |
| | `acl_id` | unique ACL id | |
| | `abstract` | abstract extracted by GROBID | |
| | `full_text` | full text extracted by GROBID | |
| | `corpus_paper_id` | Semantic Scholar ID | |
| | `pdf_hash` | sha1 hash of the pdf | |
| | `numcitedby` | number of citations from S2 | |
| | `url` | link of publication | |
| | `publisher` | - | |
| | `address` | Address of conference | |
| | `year` | - | |
| | `month` | - | |
| | `booktitle` | - | |
| | `author` | list of authors | |
| | `title` | title of paper | |
| | `pages` | - | |
| | `doi` | - | |
| | `number` | - | |
| | `volume` | - | |
| | `journal` | - | |
| | `editor` | - | |
| | `isbn` | - | |
|
|
| ## Dataset Creation |
|
|
| The corpus has all the papers in ACL anthology - as of September'22 |
|
|
| ### Source Data |
|
|
| - [ACL Anthology](aclanthology.org) |
| - [Semantic Scholar](semanticscholar.org) |
|
|
| # Additional Information |
|
|
| ### Licensing Information |
|
|
| The ACL OCL corpus is released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). By using this corpus, you are agreeing to its usage terms. |
|
|
| ### Citation Information |
|
|
| If you use this corpus in your research please use the following BibTeX entry: |
|
|
| @Misc{acl-ocl, |
| author = {Shaurya Rohatgi, Yanxia Qin, Benjamin Aw, Niranjana Unnithan, Min-Yen Kan}, |
| title = {The ACL OCL Corpus: advancing Open science in Computational Linguistics}, |
| howpublished = {arXiv}, |
| year = {2022}, |
| url = {https://huggingface.co/datasets/ACL-OCL/ACL-OCL-Corpus} |
| } |
| |
| ### Acknowledgements |
|
|
| We thank Semantic Scholar for providing access to the citation-related data in this corpus. |
|
|
| ### Contributions |
|
|
| Thanks to [@shauryr](https://github.com/shauryr), [Yanxia Qin](https://github.com/qolina) and [Benjamin Aw](https://github.com/Benjamin-Aw-93) for adding this dataset. |