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Conference proceeding
Constructing Cross-Lingual Consumer Health Vocabulary with Word-Embedding from Comparable User Generated Content
2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pp 275-284
03 Jun 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The online health community (OHC) is the primary channel for laypeople to share health information. To analyze the health consumer-generated content (HCGC) from the OHCs, identifying the colloquial medical expressions used by laypeople is a critical challenge. The open-access and collaborative consumer health vocabulary (OAC CHV) is the controlled vocabulary for addressing such a challenge. Nevertheless, OAC CHV is only available in English, limiting its applicability to other languages. This research proposes a cross-lingual automatic term recognition framework for extending the English CHV into a cross-lingual one. Our framework requires an English HCGC corpus and a non-English (i.e., Chinese in this study) HCGC corpus as inputs. Two monolingual word vector spaces are determined using the skip-gram algorithm so that each space encodes common word associations from laypeople within a language. Based on the isometry assumption, the framework aligns two monolingual spaces into a bilingual word vector space, where we employ cosine similarity as a metric for identifying semantically similar words across languages. The experimental results demonstrate that our framework outperforms the other two large language models in identifying CHV across languages. Our framework only requires raw HCGC corpora and a limited size of medical translations, reducing human efforts in compiling cross-lingual CHV.
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Details
- Title
- Constructing Cross-Lingual Consumer Health Vocabulary with Word-Embedding from Comparable User Generated Content
- Creators
- Chia-Hsuan Chang - Drexel UniversityLei Wang - Drexel UniversityChristopher C. Yang - Drexel University
- Publication Details
- 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pp 275-284
- Publisher
- IEEE
- Number of pages
- 10
- Grant note
- IIS-1741306,IIS-2235548 / National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001304501700035
- Scopus ID
- 2-s2.0-85203684529
- Other Identifier
- 991021901005004721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Medical Informatics