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research-article

FBSN: : A hybrid fine-grained neural network for biomedical event trigger identification

Authors:

Kan XuAuthors Info & Claims

Volume 381, Issue C

Pages 105 - 112

https://doi.org/10.1016/j.neucom.2019.09.042

Published: 14 March 2020 Publication History

Abstract

Biomedical event extraction is one of the fundamental tasks in medical research and disease prevention. Event trigger usually signifies the occurrence of a biomedical event by adopting a word or a phrase. Meanwhile, the task of biomedical event trigger identification is a critical and prerequisite step for biomedical event extraction. The existing methods generally rely on the complex and unobtainable features engineering. To alleviate this problem, we propose a hybrid structure FBSN which consists of Fine-grained Bidirectional Long Short Term Memory (FBi-LSTM) and Support Vector Machine (SVM) to deal with the event trigger identification. The hybrid architecture makes the most of their advantages: FBi-LSTM is to mainly extract the higher level features by the fine-grained representations, and SVM is largely appropriate for small dataset for classifying the results of biomedical event trigger. After that, the popular dataset Multi Level Event Extraction (MLEE) is employed to verify our hybrid structure. Experimental results show that our method is able to achieve the state-of-the-art baseline approaches. Meanwhile, we also discuss the detailed experiments in trigger identification task.

References

[1]

S. Ananiadou, P. Thompson, R. Nawaz, J. McNaught, Douglas B. Kell, Event-based text mining for biology and functional genomics, Brief. Funct. Genom. 14 (3) (2015) 213–230.

[2]

T. Ohta, S. Pyysalo, R. Rak, A. Rowley, H.-W. Chun, S.-J. Jung, C. Jeong, S.-P. Choi, and S. Ananiadou. 2013. Overview of the pathway curation (pc) task of bionlp shared task 2013.

[3]

S. Pyysalo, T. Ohta, M. Miwa, et al., Event extraction across multiple levels of biological organization, Bioinformatics 28 (18) (2012) i575–i581.

Digital Library

[4]

Zhou D., Zhong D., He Y., Event trigger identification for biomedical events extraction using domain knowledge, Bioinformatics 30 (11) (2014) 1587–1594.

[5]

Bui Q.C., D. Campos, E. van Mulligen, et al., A fast rule-based approach for biomedical event extraction, in: Proceedings of the BioNLP Shared Task 2013 Workshop, 2013, pp. 104–108.

[6]

J. Björne, T. Salakoski, TEES 2.2: biomedical event extraction for diverse corpora, BMC Bioinf. 16 (16) (2015) S4.

[7]

M. Miwa, P. Thompson, S. Ananiadou, Boosting automatic event extraction from the literature using domain adaptation and coreference resolution, Bioinformatics 28 (13) (2012) 1759–1765.

[8]

Wang J., Xu Q., Lin H., et al., Semi-supervised method for biomedical event extraction, Proteome Sci. 11 (1) (2013) S17.

[9]

A. MacKinlay, D. Martinez, A.J. Yepes, et al., Extracting biomedical events and modifications using subgraph matching with noisy training data, in: Proceedings of the BioNLP Shared Task 2013 Workshop, 2013, pp. 35–44.

[10]

Zhou D., Zhong D., A semi-supervised learning framework for biomedical event extraction based on hidden topics, Artif. Intell. Med. 64 (1) (2015) 51–58.

Digital Library

[11]

Wang J., Li H., An Y., et al., Biomedical event trigger detection based on convolutional neural network, Int. J. Data Min. Bioinf. 15 (3) (2016) 195–213.

[12]

S. Rao, D. Marcu, K. Knight, et al., Biomedical event extraction using abstract meaning representation, BioNLP 2017 (2017) 126–135.

[13]

Li L., Wan J., Zheng J., et al., Biomedical event extraction based on GRU integrating attention mechanism, BMC Bioinf. 19 (9) (2018) 177.

[14]

S. Ebert, T. Vu N, H. Schütze, CIS-positive: combining convolutional neural networks and SVMS for sentiment analysis in Twitter, in: Proceedings of the Ninth International Workshop on Semantic Evaluation, 2015, pp. 527–532.

[15]

M. Matsugu, K. Mori, T. Suzuki, Face recognition using SVM combined with CNN for face detection, in: Proceedings of International Conference on Neural Information Processing, 2004, pp. 356–361.

[16]

Huang D., Jiang Z., Zou L., Li L., Drug-drug interaction extraction from biomedical literature using support vector machine and long short term memory networks, Inf. Sci. 415 (2017) 100–109.

[17]

J. Pennington, R. Socher, C. Manning, Glove: global vectors for word representation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1532–1543.

[18]

Zhang X., Zhao J., LeCun Y., Character-level convolutional networks for text classification, Adv. Neural Inf. Process. Syst. (2015) 649–657.

[19]

S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (8) (1997) 1735–1780.

Digital Library

[20]

A. Graves, N. Jaitly, A. Mohamed, Hybrid speech recognition with deep bidirectional LSTM, in: Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), IEEE, 2013, pp. 273–278.

[21]

He K., Zhang X., Ren S., et al., Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.

[22]

T. Dozat. Incorporating nesterov momentum into adam. (2016)[J]. Dostupné z: http://cs229.stanford.edu/proj2015/054_report.pdf, 2016.

[23]

Wang J., Li H., An Y., Lin H., Yang Z., Biomedical event trigger detection based on convolutional neural network, Int. J. Data Min. Bioinf. 15 (3) (2016) 195–213.

[24]

P.V.S.S. Rahul, S.K. Sahu, A. Anand, Biomedical event trigger identification using bidirectional recurrent neural network based models[J]. arXiv preprint arXiv:1705.09516, 2017.

[25]

Li L., Liu Y., Qin M., Extracting biomedical events with parallel multi-pooling convolutional neural networks, IEEE/ACM Trans. Comput. Biol. Bioinf. (2018).

[26]

Liu J., Chen Y., Liu K., et al., Event detection via gated multilingual attention mechanism, in: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

Cited By

Lv JZhang ZXu GSun XLi SLiu QDong P(2023)Exploiting event-aware and role-aware with tree pruning for document-level event extractionNeural Computing and Applications10.1007/s00521-023-08282-w35:15(11061-11072)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s00521-023-08282-w

Index Terms

FBSN: A hybrid fine-grained neural network for biomedical event trigger identification
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Neurocomputing

Neurocomputing Volume 381, Issue C

Mar 2020

349 pages

ISSN:0925-2312

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Elsevier Ltd.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 14 March 2020

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Cited By

Lv JZhang ZXu GSun XLi SLiu QDong P(2023)Exploiting event-aware and role-aware with tree pruning for document-level event extractionNeural Computing and Applications10.1007/s00521-023-08282-w35:15(11061-11072)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s00521-023-08282-w

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