Journal article
Biomedical event trigger detection based on convolutional neural network
International journal of data mining and bioinformatics, v 15(3)
01 Jan 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Event trigger detection, which plays a key role in biomedical event extraction, has attracted significant attention recently. However, most approaches are based on statistical models, much relying on complex hand-designed features. In this paper, we utilise the ability of Convolutional Neural Network (CNN) for addressing higher-level features automatically to explore correlations between a trigger and an event type. We only keep one candidate trigger along with N-words around it and entity mention features as a raw input, giving up complex input with hand-designed features that derived from currently existed Natural Language Processing (NLP) tools. Our experiments on Multi-Level Event Extraction (MLEE) corpus showed that the method achieved a higher F-score of 78.67% compared to the state-of-the-art approaches. The results demonstrate that the proposed method is effective for event trigger detection.
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Details
- Title
- Biomedical event trigger detection based on convolutional neural network
- Creators
- Jian Wang - Dalian University of TechnologyHonglei Li - Dalian University of TechnologyYuan An - Drexel UniversityHongfei Lin - Dalian University of TechnologyZhihao Yang - Dalian University of Technology
- Publication Details
- International journal of data mining and bioinformatics, v 15(3)
- Publisher
- Inderscience Enterprises Ltd
- Number of pages
- 19
- Grant note
- NCET-13-0084 / Trans-Century Training Program Foundation for the Talents by the Ministry of Education of China DUT13JB09 / Fundamental Research Funds for the Central Universities 61572098; 61572102; 61272373; 61300088 / Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000378614300001
- Scopus ID
- 2-s2.0-84975842649
- Other Identifier
- 991019167334104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology