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Trigger Words Detection by Integrating Attention Mechanism into Bi-LSTM Neural Network—A Case Study in PubMED-Wide Trigger Words Detection for Pancreatic Cancer

  • Kaiyin Zhou
  • Xinzhi Yao
  • Shuguang Wang
  • Jin-Dong Kim
  • Kevin Bretonnel Cohen
  • Ruiying Chen
  • Yuxing Wang
  • Jingbo Xia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)

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.

Keywords

Natural language processing LSTM Encoder/decoder model Trigger words 

Notes

Acknowledgement

This work is funded by the Fundamental Research Funds for the Central Universities of China (Project No. 2662018PY096).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kaiyin Zhou
    • 1
    • 2
  • Xinzhi Yao
    • 1
  • Shuguang Wang
    • 1
  • Jin-Dong Kim
    • 3
  • Kevin Bretonnel Cohen
    • 4
  • Ruiying Chen
    • 1
  • Yuxing Wang
    • 1
    • 2
  • Jingbo Xia
    • 1
    • 2
  1. 1.College of InformaticsHuazhong Agricultural UniversityWuhanChina
  2. 2.Hubei Key Laboratory of Agricultural BioinformaticsWuhanChina
  3. 3.Database Center for Life Science (DBCLS)Research Organization of Information and Systems (ROIS)TokyoJapan
  4. 4.School of MedicineUniversity of Colorado DenverAuroraUSA

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