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| "paper_id": "2020", |
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| "date_generated": "2023-01-19T03:12:15.027191Z" |
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| "text": "Welcome to MultilingualBIO 2020, the LREC2020 Workshop on \"Multilingual Biomedical Text Processing\" . As the COVID-19 pandemic unrolls during the first months of 2020 around the world, the need for strong AI and NLP technologies for biomedical text is more evident than ever. As in other NLP areas, we are currently witnessing fast developments, with improved access, analysis and integration of healthcare-relevant information from heterogeneous content types, including electronic health records, medical literature, clinical trials, medical agency reports or patient-reported information available form social media and forums. There is an increasing automation of tasks in many critical areas, such as detecting interactions or supporting clinical decision. However, progress is very uneven depending on the language. Main achievements in processing biomedical text are almost restricted to English, with most other languages lagging behind in this respect, due to lack of annotated resources, incomplete vocabularies and insufficient in-domain corpora. More effort from the research community is needed to endow these languages with the necessary resources. The second edition of MultilingualBIO, at the LREC 2020 Conference, aims at promoting the development of biomedical text processing resources and components in languages beyond English, exploring the use of novel methodological advances for a diversity of tasks in the domain. ", |
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| "section": "Introduction", |
| "sec_num": null |
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| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "ICONIC (Ireland)", |
| "authors": [ |
| { |
| "first": "Patrik", |
| "middle": [], |
| "last": "Lambert", |
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| "year": null, |
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| "num": null, |
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| "raw_text": "Patrik Lambert, ICONIC (Ireland)", |
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| "text": "Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining Maria Bampa and Hercules Dalianis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Building a Norwegian Lexical Resource for Medical Entity Recognition Ildiko Pilan, P\u00e5l H. Brekke and Lilja \u00d8vrelid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Localising the Clinical Terminology SNOMED CT by Semi-automated Creation of a German Interface Vocabulary Stefan Schulz, Larissa Hammer, David Hashemian-Nik and Markus Kreuzthaler . . . . . . . . . . . . . . 15 Multilingual enrichment of disease biomedical ontologies L\u00e9o Bouscarrat, Antoine Bonnefoy, C\u00e9cile Capponi and Carlos Ramisch . . . . . . . . . . . . . . . . . . . . . 21 Transfer learning applied to text classification in Spanish radiological reports Pilar L\u00f3pez \u00dabeda, Manuel Carlos D\u00edaz-Galiano, L. Alfonso Urena Lopez, Maite Martin, Teodoro Mart\u00edn-Noguerol and Antonio Luna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Automated Processing of Multilingual Online News for the Monitoring of Animal Infectious Diseases Sarah Valentin, Renaud Lancelot and Mathieu Roche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 v", |
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