# Context awareness and embedding for biomedical event extraction

## TL;DR

This paper introduces a scalable, context-aware framework for biomedical event extraction that leverages bi-directional LSTM embeddings to significantly improve detection accuracy over existing methods.

## Contribution

It proposes a novel bottom-up detection framework with context embedding via bi-directional LSTM, achieving state-of-the-art performance on biomedical event datasets.

## Key findings

- Achieved average F-score of 0.92 on BioNLPST-BB dataset.
- Nearly doubled the F-score compared to previous methods on the same dataset.
- Demonstrated the effectiveness of context-aware embeddings in biomedical event detection.

## Abstract

Motivation: Biomedical event detection is fundamental for information extraction in molecular biology and biomedical research. The detected events form the central basis for comprehensive biomedical knowledge fusion, facilitating the digestion of massive information influx from literature. Limited by the feature context, the existing event detection models are mostly applicable for a single task. A general and scalable computational model is desiderated for biomedical knowledge management. Results: We consider and propose a bottom-up detection framework to identify the events from recognized arguments. To capture the relations between the arguments, we trained a bi-directional Long Short-Term Memory (LSTM) network to model their context embedding. Leveraging the compositional attributes, we further derived the candidate samples for training event classifiers. We built our models on the datasets from BioNLP Shared Task for evaluations. Our method achieved the average F-scores of 0.81 and 0.92 on BioNLPST-BGI and BioNLPST-BB datasets respectively. Comparing with 7 state-of-the-art methods, our method nearly doubled the existing F-score performance (0.92 vs 0.56) on the BioNLPST-BB dataset. Case studies were conducted to reveal the underlying reasons. Availability: https://github.com/cskyan/evntextrc