- Kaiyin Zhou18,19,
- Xinzhi Yao18,
- Shuguang Wang18,
- Jin-Dong Kim20,
- Kevin Bretonnel Cohen21,
- Ruiying Chen18,
- Yuxing Wang18,19 &
- …
- Jingbo Xia18,19
Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 11221))
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Abstract
A Bi-LSTM based encode/decode mechanism for named entity recognition was studied in this research. In the proposed mechanism, Bi-LSTM was used for encoding, an Attention method was used in the intermediate layers, and an unidirectional LSTM was used as decoder layer. By using element wise product to modify the conventional decoder layers, the proposed model achieved better F-score, compared with other three baseline LSTM-based models. For the purpose of algorithm application, a case study of causal gene discovery in terms of disease pathway enrichment was designed. In addition, the causal gene discovery rate of our proposed method was compared with another baseline methods. The result showed that trigger genes detection effectively increase the performance of a text mining system for causal gene discovery.
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Acknowledgement
This work is funded by the Fundamental Research Funds for the Central Universities of China (Project No. 2662018PY096).
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Authors and Affiliations
College of Informatics, Huazhong Agricultural University, Wuhan, China
Kaiyin Zhou, Xinzhi Yao, Shuguang Wang, Ruiying Chen, Yuxing Wang & Jingbo Xia
Hubei Key Laboratory of Agricultural Bioinformatics, Wuhan, China
Kaiyin Zhou, Yuxing Wang & Jingbo Xia
Database Center for Life Science (DBCLS), Research Organization of Information and Systems (ROIS), Tokyo, Japan
Jin-Dong Kim
School of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, USA
Kevin Bretonnel Cohen
- Kaiyin Zhou
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- Jingbo Xia
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Correspondence toJingbo Xia.
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Tsinghua University, Beijing, China
Maosong Sun
Harbin Institute of Technology, Harbin, China
Ting Liu
Beijing University of Posts and Telecommunications, Beijing, China
Xiaojie Wang
Tsinghua University, Beijing, China
Zhiyuan Liu
Tsinghua University, Beijing, China
Yang Liu
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Zhou, K.et al. (2018). Trigger Words Detection by Integrating Attention Mechanism into Bi-LSTM Neural Network—A Case Study in PubMED-Wide Trigger Words Detection for Pancreatic Cancer. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_33
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