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
Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvist. Investig. 30(1), 326 (2007)
Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need (2017)
Sintchenko, V., Anthony, S., Phan, X.H., Lin, F., Coiera, E.W.: A PubMed-wide associational study of infectious diseases. PLoS One 5(3), e9535 (2010)
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)
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)
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)
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
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)
Zheng, S., Hao, Y., Lu, D., et al.: Joint entity and relation extraction based on a hybrid neural network. Neurocomputing 257, 1–8 (2017)
Liu, B., Lane, I.: Attention-based recurrent neural network models for joint intent detection and slot filling. arXiv preprint arXiv:1609.01454, 6 September 2016
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)
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)
Wang, Z.Y., Zhang, H.Y.: Rational drug repositioning by medical genetics. Nat. Biotechnol. 31(12), 1080–1082 (2013)
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF (2016)
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)
Pavlopoulos, I., Kosmopoulos, A., Androutsopoulos, I.: Continuous space word vectors obtained by applying Word2Vec to abstracts of biomedical articles (2014)
Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
Jean, S., Cho, K., Memisevic, R., Bengio, Y.: On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007 (2014)
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)
Acknowledgement
This work is funded by the Fundamental Research Funds for the Central Universities of China (Project No. 2662018PY096).
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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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