Comparing neural models for nested and overlapping biomedical event detection.

ABSTRACT:

Background

Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural models: a novel EXhaustive Neural Network (EXNN) and a Search-Based Neural Network (SBNN) for detection of nested and overlapping events.

Results

We evaluate the proposed models as an event detection component in isolation and within a pipeline setting. Evaluation in several annotated biomedical event extraction datasets shows that both EXNN and SBNN achieve higher performance in detecting nested and overlapping events, compared to

SUBMITTER: Espinosa K 

PROVIDER: S-EPMC9161617 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature