Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

Rahul V S S Patchigolla, Sunil Sahu, Ashish Anand


Abstract
Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex hand-crafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task.
Anthology ID:
W17-2340
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
316–321
Language:
URL:
https://www.aclweb.org/anthology/W17-2340
DOI:
10.18653/v1/W17-2340
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2340.pdf