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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Biomedical event trigger detection by dependency-based word embedding
BMC medical genomics, v 9 Suppl 2(Suppl 2), pp 45-45
10 Aug 2016
PMID: 27510445
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In biomedical research, events revealing complex relations between entities play an important role. Biomedical event trigger identification has become a research hotspot since its important role in biomedical event extraction. Traditional machine learning methods, such as support vector machines (SVM) and maxent classifiers, which aim to manually design powerful features fed to the classifiers, depend on the understanding of the specific task and cannot generalize to the new domain or new examples.
In this paper, we propose an approach which utilizes neural network model based on dependency-based word embedding to automatically learn significant features from raw input for trigger classification. First, we employ Word2vecf, the modified version of Word2vec, to learn word embedding with rich semantic and functional information based on dependency relation tree. Then neural network architecture is used to learn more significant feature representation based on raw dependency-based word embedding. Meanwhile, we dynamically adjust the embedding while training for adapting to the trigger classification task. Finally, softmax classifier labels the examples by specific trigger class using the features learned by the model.
The experimental results show that our approach achieves a micro-averaging F1 score of 78.27 and a macro-averaging F1 score of 76.94 % in significant trigger classes, and performs better than baseline methods. In addition, we can achieve the semantic distributed representation of every trigger word.
Metrics
Details
- Title
- Biomedical event trigger detection by dependency-based word embedding
- Creators
- Jian Wang - Dalian University of TechnologyJianhai Zhang - Dalian University of TechnologyYuan An - Drexel UniversityHongfei Lin - Dalian University of TechnologyZhihao Yang - Dalian University of TechnologyYijia Zhang - Dalian University of TechnologyYuanyuan Sun - Dalian University of Technology
- Publication Details
- BMC medical genomics, v 9 Suppl 2(Suppl 2), pp 45-45
- Publisher
- Springer BMC
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000381319600003
- Scopus ID
- 2-s2.0-84981249334
- Other Identifier
- 991019167703304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Genetics & Heredity