The Hmong Medical Corpus: a biomedical corpus for a minority language
White, Nathan (2022) The Hmong Medical Corpus: a biomedical corpus for a minority language. Language Resources and Evaluation, 56. pp. 1315-1332.
|
PDF (Published Version)
- Published Version
Available under License Creative Commons Attribution. Download (700kB) | Preview |
Abstract
Biomedical communication is an area that increasingly benefits from natural language processing (NLP) work. Biomedical named entity recognition (NER) in particular provides a foundation for advanced NLP applications, such as automated medical question-answering and translation services. However, while a large body of biomedical documents are available in an array of languages, most work in biomedical NER remains in English, with the remainder in official national or regional languages. Minority languages so far remain an underexplored area. The Hmong language, a minority language with sizable populations in several countries and without official status anywhere, represents an exceptional challenge for effective communication in medical contexts. Taking advantage of the large number of government-produced medical information documents in Hmong, we have developed the first named entity-annotated biomedical corpus for a resource-poor minority language. The Hmong Medical Corpus contains 100,535 tokens with 4554 named entities (NEs) of three UMLS semantic types: diseases/syndromes, signs/symptoms, and body parts/organs/organ components. Furthermore, a subset of the corpus is annotated for word position and parts of speech, representing the first such gold-standard dataset publicly available for Hmong. The methodology presented provides a readily reproducible approach for the creation of biomedical NE-annotated corpora for other resource-poor languages.
| Item ID: | 74199 |
|---|---|
| Item Type: | Article (Research - C1) |
| ISSN: | 1574-0218 |
| Keywords: | biomedical corpus, named entity recognition, language models, machine learning, Hmong, minority languages |
| Related URLs: | |
| Copyright Information: | © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licen ses/by/4.0/. |
| Date Deposited: | 13 Jul 2022 23:30 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100% |
| SEO Codes: | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 50% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50% |
| Downloads: |
Total: 674 Last 12 Months: 12 |
| More Statistics |
Actions (Repository Staff Only)
 |
Item Control Page |