Centrala begrepp
N-ary relation extraction improves performance of Binding events in pipelined biomedical event extraction.
Sammanfattning
The content discusses the challenges and approaches in biomedical event extraction, focusing on the pipelined approach and joint learning methods. It introduces an n-ary relation extraction method based on the BERT pre-training model to construct Binding events, improving overall performance. The structure of the content includes an introduction to biomedical event extraction, challenges faced, mainstream frameworks, and detailed explanations of trigger identification, argument role recognition, and rule-based event construction. Experimental results show significant improvements in F1 scores for GE11 and GE13 corpora using the proposed method.
Introduction
- Biomedical event extraction aims to extract events from text.
- Structured information includes entities, relations, and events.
- Challenges include ambiguity in triggers and nested events.
Pipelined Approach vs. Joint Learning
- Pipelined approach decomposes tasks into trigger identification, argument recognition, and event construction.
- Joint learning combines tasks to overcome cascading errors.
Rule-Based Event Construction
- Different types of events have specific argument compositions.
- Rules are used for isolated triggers, simple events, multiple events, and nested events.
Automatic Event Construction with N-ary Relation Extraction
- Proposal of n-ary relation extraction method for Binding events.
- Framework includes input layer, BERT encoder, and output layer.
Experimentation & Results
- Comparison of rule-based vs. automatic construction scenarios.
- Performance comparison across different event types.
Precision Increase & Recall Comparison
- Precision increase due to reduced false positives in Binding event extraction.
- Recall differences between GE11 and GE13 datasets analyzed.
Statistik
"Our method achieves promising results on the GE11 and GE13 corpora with F1 scores of 63.14% and 59.40%, respectively."
Citat
"The experimental results show that our method achieves promising results on the GE11 and GE13 corpora."
"Most deep learning methods have higher performance than rule-based approaches."
"Our approach is potentially used to improve the performance of joint learning models."