Summary of Pipelined Biomedical Event Extraction Rivaling Joint Learning, by Pengchao Wu et al.
Pipelined Biomedical Event Extraction Rivaling Joint Learning
by Pengchao Wu, Xuefeng Li, Jinghang Gu, Longhua Qian, Guodong Zhou
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to biomedical event extraction, leveraging the BERT pre-training model for n-ary relation extraction. The goal is to capture semantic information about an event’s context and participants by constructing Binding events. Unlike traditional pipelined approaches, this method directly models the complex relationships between entities in text. Experimental results on the GE11 and GE13 corpora of the BioNLP shared task show promising F1 scores of 63.14% and 59.40%, respectively. The proposed approach not only improves Binding event performance but also rivals joint learning methods, demonstrating its potential for pipelined event extraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are working on a new way to understand what’s happening in medical texts. They want to identify events, like a patient getting diagnosed or treated, and capture the important details about each event. The old way of doing this involved breaking down the process into smaller steps. But this new approach uses a special model called BERT to look at the text as a whole and find the relationships between different parts. By using this method, they were able to get good results on two big datasets of medical texts. This could help make it easier for computers to understand medical information in the future. |
Keywords
» Artificial intelligence » Bert
