Biomedical event extraction via Long Short Term Memory networks along dynamic extended tree

Extracting knowledge from unstructured text is one of the most important goals of Natural Language Processing, especially in biomedical event extraction domain. In this paper, we describe a system for extracting biomedical events among biotope and bacteria from biomedical literature, using the corpus from the BioNLP'16 Shared Task on Bacteria Biotope task. The current mainstream methods for event extraction are based on shallow machine learning methods. However, these methods mainly rely on domain experience and need enormous manual efforts to select features. Therefore, we propose a novel Long Short Term Memory (LSTM) Networks framework DETBLSTM for event extraction. In our framework, a dynamic extended tree is introduced as the input instead of the original sentences, which utilizes the syntactic information. Furthermore, the POS and distance embeddings are added to enrich input information and thus the complex feature extraction can be skipped. In final, we construct a bidirectional LSTM model to extract biomedical events and achieve 57.14% F-score in the test set. Our model obtains a better F-score than all official submissions to BioNLP-ST 2016, which is 1.34% higher than the best system.