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2016 | OriginalPaper | Buchkapitel

Biomedical Event Trigger Detection Based on Hybrid Methods Integrating Word Embeddings

verfasst von : Lishuang Li, Meiyue Qin, Degen Huang

Erschienen in: Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data

Verlag: Springer Singapore

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Abstract

Trigger detection as the preceding task is of great importance in biomedical event extraction. By now, most of the state-of-the-art systems have been based on single classifiers, and the words encoded by one-hot are unable to represent the semantic information. In this paper, we utilize hybrid methods integrating word embeddings to get higher performance. In hybrid methods, first, multiple single classifiers are constructed based on rich manual features including dependency and syntactic parsed results. Then multiple predicting results are integrated by set operation, voting and stacking method. Hybrid methods can take advantage of the difference among classifiers and make up for their deficiencies and thus improve performance. Word embeddings are learnt from large scale unlabeled texts and integrated as unsupervised features into other rich features based on dependency parse graphs, and thus a lot of semantic information can be represented. Experimental results show our method outperforms the state-of-the-art systems.

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Literatur
1.
Zurück zum Zitat Björne, J., Heimonen, J., Ginter, F., Airola, A., Pahikkala, T., Salakoski, T.: Extracting complex biological events with rich graph-based feature sets. In: Proceedings of Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task, pp. 10–18. ACL, Boulder, Colorado (2009) Björne, J., Heimonen, J., Ginter, F., Airola, A., Pahikkala, T., Salakoski, T.: Extracting complex biological events with rich graph-based feature sets. In: Proceedings of Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task, pp. 10–18. ACL, Boulder, Colorado (2009)
2.
Zurück zum Zitat Martinez, D., Baldwin, T.: Word sense disambiguation for event trigger word detection in biomedicine. BMC Bioinform. 12(Suppl. 2), S4 (2011)CrossRef Martinez, D., Baldwin, T.: Word sense disambiguation for event trigger word detection in biomedicine. BMC Bioinform. 12(Suppl. 2), S4 (2011)CrossRef
3.
Zurück zum Zitat Zhang, Y., Lin, H., Yang, Z., Wang, J., Li, Y.: Biomolecular event trigger detection using neighborhood hash features. J. Theoret. Biol. 318, 22–28 (2013)CrossRef Zhang, Y., Lin, H., Yang, Z., Wang, J., Li, Y.: Biomolecular event trigger detection using neighborhood hash features. J. Theoret. Biol. 318, 22–28 (2013)CrossRef
4.
Zurück zum Zitat Majumder, A.: Multiple features based approach to extract bio-molecular event triggers using conditional random field. Int. J. Intell. Syst. Appl. 4(12), 41–47 (2012) Majumder, A.: Multiple features based approach to extract bio-molecular event triggers using conditional random field. Int. J. Intell. Syst. Appl. 4(12), 41–47 (2012)
5.
Zurück zum Zitat Wang, J., Wu, Y., Lin, H., Yang, Z.: Biological event trigger extraction based on deep parsing. Comput. Eng. 39, 25–30 (2013) Wang, J., Wu, Y., Lin, H., Yang, Z.: Biological event trigger extraction based on deep parsing. Comput. Eng. 39, 25–30 (2013)
6.
Zurück zum Zitat Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)CrossRef Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)CrossRef
7.
Zurück zum Zitat Li, L., Fan, W., Huang, D., Dang, Y., Sun, J.: Boosting performance of gene mention tagging system by hybrid methods. J. Biomed. Inf. 45(1), 156–164 (2012)CrossRef Li, L., Fan, W., Huang, D., Dang, Y., Sun, J.: Boosting performance of gene mention tagging system by hybrid methods. J. Biomed. Inf. 45(1), 156–164 (2012)CrossRef
8.
Zurück zum Zitat Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)MathSciNetMATH Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)MathSciNetMATH
10.
Zurück zum Zitat Tang, B., Cao, H., Wang, X., Chen, Q., Xu, H.: Evaluating word representation features in biomedical named entity recognition tasks. BioMed. Res. Int. 2014, Article ID 240403, 1–6 (2014). Hindawi Publishing Corporation Tang, B., Cao, H., Wang, X., Chen, Q., Xu, H.: Evaluating word representation features in biomedical named entity recognition tasks. BioMed. Res. Int. 2014, Article ID 240403, 1–6 (2014). Hindawi Publishing Corporation
11.
Zurück zum Zitat Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, pp. 384–394 (2010) Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, pp. 384–394 (2010)
12.
Zurück zum Zitat Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P., Collins, M.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)MATH Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P., Collins, M.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)MATH
13.
Zurück zum Zitat Mnih, A., Hinton, G.: A scalable hierarchical distributed language model. In: NIPS, pp. 1081–1088 (2008) Mnih, A., Hinton, G.: A scalable hierarchical distributed language model. In: NIPS, pp. 1081–1088 (2008)
14.
Zurück zum Zitat Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013)
15.
Zurück zum Zitat Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of NAACL-HLT, Atlanta, Georgia, pp. 746–751 (2013) Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of NAACL-HLT, Atlanta, Georgia, pp. 746–751 (2013)
16.
Zurück zum Zitat McClosky, D., Charniak, E.: Self-training for biomedical parsing. In: Proceedings of 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies, Columbus, Ohio, pp. 101–104 (2008) McClosky, D., Charniak, E.: Self-training for biomedical parsing. In: Proceedings of 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies, Columbus, Ohio, pp. 101–104 (2008)
17.
Zurück zum Zitat Miyao, Y., Sagae, K., Saetre, R., Matsuzaki, T., Tsujii, J.: Evaluating contributions of natural language parsers to protein–protein interaction extraction. Bioinformatics 25(3), 394–400 (2009)CrossRef Miyao, Y., Sagae, K., Saetre, R., Matsuzaki, T., Tsujii, J.: Evaluating contributions of natural language parsers to protein–protein interaction extraction. Bioinformatics 25(3), 394–400 (2009)CrossRef
19.
20.
Zurück zum Zitat Kim, J.D., Ohta, T., Pyysalo, S., Kano, Y., Tsujii, J.: Overview of BioNLP’09 shared task on event extraction. In: Proceedings of Workshop on BioNLP: Shared Task, Boulder, Colorado, pp. 1–9 (2009) Kim, J.D., Ohta, T., Pyysalo, S., Kano, Y., Tsujii, J.: Overview of BioNLP’09 shared task on event extraction. In: Proceedings of Workshop on BioNLP: Shared Task, Boulder, Colorado, pp. 1–9 (2009)
21.
Zurück zum Zitat Kim, J.D., Pyysalo, S., Ohta, T., Bossy, R., Nguyen, N., Tsujii, J.: Overview of BioNLP shared task 2011. In: Proceedings of BioNLP Shared Task 2011 Workshop, pp. 1–6. Association for Computational Linguistics, Portland (2011) Kim, J.D., Pyysalo, S., Ohta, T., Bossy, R., Nguyen, N., Tsujii, J.: Overview of BioNLP shared task 2011. In: Proceedings of BioNLP Shared Task 2011 Workshop, pp. 1–6. Association for Computational Linguistics, Portland (2011)
Metadaten
Titel
Biomedical Event Trigger Detection Based on Hybrid Methods Integrating Word Embeddings
verfasst von
Lishuang Li
Meiyue Qin
Degen Huang
Copyright-Jahr
2016
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-3168-7_7