Deep learning for biomedical event extraction
Biomedical Event Extraction (BEE) is a crucial task in biomedical natural language processing, aiming to identify molecular events involving genes, proteins, and other biological entities. This thesis presents two approaches to improve BEE: a classification-based model for event trigger detection an...
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| Format: | Thesis-Master by Research |
| Language: | English |
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Nanyang Technological University
2025
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| Online Access: | https://hdl.handle.net/10356/182531 |
| _version_ | 1826116189811638272 |
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| author | Yuan, Haohan |
| author2 | Hui Siu Cheung |
| author_facet | Hui Siu Cheung Yuan, Haohan |
| author_sort | Yuan, Haohan |
| collection | NTU |
| description | Biomedical Event Extraction (BEE) is a crucial task in biomedical natural language processing, aiming to identify molecular events involving genes, proteins, and other biological entities. This thesis presents two approaches to improve BEE: a classification-based model for event trigger detection and a generation-based model for event extraction. First, the BioLSL model enhances event trigger detection by leveraging label-based synergistic representation learning, capturing dependencies between event type labels and trigger words. Experimental results on three benchmark BioNLP datasets demonstrate its state-of-the-art performance, particularly in data-scarce scenarios. Second, the GenBEE model formulates BEE as a sequence generation problem, integrating structured prompts and prefix-based representations to incorporate event semantics and argument dependencies. Besides, the structured prompts and prefix-guided learning further improve model performance by effectively integrating event structure into the generative framework, leading to more accurate and comprehensive event extraction across multiple datasets. |
| first_indexed | 2025-03-09T11:35:08Z |
| format | Thesis-Master by Research |
| id | ntu-10356/182531 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T11:35:08Z |
| publishDate | 2025 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1825312025-03-04T02:57:33Z Deep learning for biomedical event extraction Yuan, Haohan Hui Siu Cheung College of Computing and Data Science ASSCHUI@ntu.edu.sg Computer and Information Science Biomedical Event Extraction (BEE) is a crucial task in biomedical natural language processing, aiming to identify molecular events involving genes, proteins, and other biological entities. This thesis presents two approaches to improve BEE: a classification-based model for event trigger detection and a generation-based model for event extraction. First, the BioLSL model enhances event trigger detection by leveraging label-based synergistic representation learning, capturing dependencies between event type labels and trigger words. Experimental results on three benchmark BioNLP datasets demonstrate its state-of-the-art performance, particularly in data-scarce scenarios. Second, the GenBEE model formulates BEE as a sequence generation problem, integrating structured prompts and prefix-based representations to incorporate event semantics and argument dependencies. Besides, the structured prompts and prefix-guided learning further improve model performance by effectively integrating event structure into the generative framework, leading to more accurate and comprehensive event extraction across multiple datasets. Master's degree 2025-02-07T01:32:37Z 2025-02-07T01:32:37Z 2025 Thesis-Master by Research Yuan, H. (2025). Deep learning for biomedical event extraction. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/182531 https://hdl.handle.net/10356/182531 10.32657/10356/182531 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Yuan, Haohan Deep learning for biomedical event extraction |
| title | Deep learning for biomedical event extraction |
| title_full | Deep learning for biomedical event extraction |
| title_fullStr | Deep learning for biomedical event extraction |
| title_full_unstemmed | Deep learning for biomedical event extraction |
| title_short | Deep learning for biomedical event extraction |
| title_sort | deep learning for biomedical event extraction |
| topic | Computer and Information Science |
| url | https://hdl.handle.net/10356/182531 |
| work_keys_str_mv | AT yuanhaohan deeplearningforbiomedicaleventextraction |