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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

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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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References

  1. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvist. Investig.30(1), 326 (2007)

    Google Scholar 

  2. Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need (2017)

    Google Scholar 

  3. Sintchenko, V., Anthony, S., Phan, X.H., Lin, F., Coiera, E.W.: A PubMed-wide associational study of infectious diseases. PLoS One5(3), e9535 (2010)

    Article  Google Scholar 

  4. Allot, A., Peng, Y., Wei, C.H., Lee, K., Phan, L., Lu, Z.: LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC. Nucl. Acids Res.46(W1), W530–W536 (2018)

    Article  Google Scholar 

  5. Cohen, K.B., et al.: High-precision biological event extraction with a concept recognizer. In: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task, 5 June 2009, pp. 50–58. Association for Computational Linguistics (2009)

    Google Scholar 

  6. Song, M., Kim, M., Kang, K., Kim, Y.H., Jeon, S.: Application of public knowledge discovery tool (PKDE4J) to represent biomedical scientific knowledge. Front. Res. Metr. Anal.3, 7 (2018)

    Article  Google Scholar 

  7. Zhou, H., Yang, Y., Ning, S., Liu, Z., Lang, C., Lin, Y., Huang, D.: Combining context and knowledge representations for chemical-disease relation extraction. IEEE/ACM Trans. Comput. Biol. Bioinform. (2018).https://doi.org/10.1109/TCBB.2018.2838661

  8. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprintarXiv:1406.1078 (2014)

  9. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprintarXiv:1409.0473 (2014)

  10. Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprintarXiv:1508.01991 (2015)

  11. Zheng, S., Hao, Y., Lu, D., et al.: Joint entity and relation extraction based on a hybrid neural network. Neurocomputing257, 1–8 (2017)

    Article  Google Scholar 

  12. Liu, B., Lane, I.: Attention-based recurrent neural network models for joint intent detection and slot filling. arXiv preprintarXiv:1609.01454, 6 September 2016

  13. Wang, Y., et al.: Guideline design of an active gene annotation corpus for the purpose of drug repurposing. In: OHDSI 2018 Workshop, July, Guangzhou (2018, submitted)

    Google Scholar 

  14. Kim, J.D., Wang, Y.: PubAnnotation: a persistent and sharable corpus and annotation repository. In: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing, pp. 202–205. Association for Computational Linguistics (2012)

    Google Scholar 

  15. Wang, Z.Y., Zhang, H.Y.: Rational drug repositioning by medical genetics. Nat. Biotechnol.31(12), 1080–1082 (2013)

    Article  Google Scholar 

  16. Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF (2016)

    Google Scholar 

  17. Huang, E.H., Socher, R., Manning, C.D., Ng, A.Y.: Improving word representations via global context and multiple word prototypes. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 873–882. Association for Computational Linguistics (2012)

    Google Scholar 

  18. Pavlopoulos, I., Kosmopoulos, A., Androutsopoulos, I.: Continuous space word vectors obtained by applying Word2Vec to abstracts of biomedical articles (2014)

    Google Scholar 

  19. Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprintarXiv:1508.04025 (2015)

  20. Jean, S., Cho, K., Memisevic, R., Bengio, Y.: On using very large target vocabulary for neural machine translation. arXiv preprintarXiv:1412.2007 (2014)

  21. Wei, C.H., Kao, H.Y., Lu, Z.: PubTator: a web-based text mining tool for assisting biocuration. Nucl. Acids Res.41(W1), W518–W522 (2013)

    Article  Google Scholar 

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Acknowledgement

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

Author information

Authors and Affiliations

  1. College of Informatics, Huazhong Agricultural University, Wuhan, China

    Kaiyin Zhou, Xinzhi Yao, Shuguang Wang, Ruiying Chen, Yuxing Wang & Jingbo Xia

  2. Hubei Key Laboratory of Agricultural Bioinformatics, Wuhan, China

    Kaiyin Zhou, Yuxing Wang & Jingbo Xia

  3. Database Center for Life Science (DBCLS), Research Organization of Information and Systems (ROIS), Tokyo, Japan

    Jin-Dong Kim

  4. School of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, USA

    Kevin Bretonnel Cohen

Authors
  1. Kaiyin Zhou

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  2. Xinzhi Yao

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  3. Shuguang Wang

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  4. Jin-Dong Kim

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  5. Kevin Bretonnel Cohen

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  6. Ruiying Chen

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  7. Yuxing Wang

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  8. Jingbo Xia

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Corresponding author

Correspondence toJingbo Xia.

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Harbin Institute of Technology, Harbin, China

    Ting Liu

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Xiaojie Wang

  4. Tsinghua University, Beijing, China

    Zhiyuan Liu

  5. 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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