Abstract
Biomedical event extraction is an important and challenging task in Information Extraction, which plays an important role for medicine research and disease prevention. Trigger identification has attracted much attention as the prerequisite step in biomedical event extraction. To skip the manual complex feature extraction, we propose a trigger identification method based on Bidirectional Long Short Term Memory (BLSTM) neural network. To obtain more semantic and syntactic information, we train dependency-based word embeddings to represent words, and add sentence embeddings to enrich sentence-level features. In addition, the attention mechanism is integrated to capture the most important semantic information in the sentence. The experimental results on the multi-level event extraction (MLEE) corpus show that the proposed method outperforms the state-of-the-art systems, achieving an F-score of 79.96%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
