Abstract
In this paper we present an efficient method for event extraction in bio-medical text. Event extraction deals with finding more detailed biological phenomenon, which is more challenging compared to simple binary relation extraction like protein-protein interaction (PPI). The task can be thought of as comprising of the following sub-problems, viz., trigger detection, classification and argument extraction. Event trigger detection and classification deal with identification of event expressions from text and classification of them into nine different categories. We use Support Vector Machine (SVM) as the base learning algorithm, which is trained with a diverse set of features that cover both statistical and linguistic characteristics. We develop a feature selection algorithm using Genetic Algorithm (GA) for determining the most relevant set of features. Experiments on benchmark datasets of BioNLP-2011 shared task datasets show the recall, precision and F-measure values of 48.17%, 65.86% and 55.64% respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
We optimize weights using development set.
- 4.
- 5.
References
Chinchor, N.: Message understanding conference (muc-7) proceedings. In: Overview of MUC-7/MET-2 (1998)
Voorhees, E.: Overview of TREC 2007. In: Sixteenth Text REtrieval Conference (TREC 2007) Proceedings (2007)
Nedellec, C.: Learning language in logic-genic interaction extraction challenge. In: Proceedings of the 4th Learning Language in Logic Workshop (LLL05), pp. 31–37 (2005)
Lynette Hirschman, M.K., Valencia, A.: Proceedings of the second biocreative challenge evaluation workshop. In: CNIO Centro Nacional de Investigaciones Oncologicas (2007)
Kim, J.-D., Ohta, T., Pyysalo, S., Kano, Y., Tsujii, J.: Overview of BioNLP’09 shared task on event extraction. In: Proceedings of the Workshop on BioNLP, BioNLP 09, pp. 1–9 (2009)
Lee, H.-G., Cho, H.-C., Kim, M.-J., Lee, J.-Y., Hong, G., Rim, H.-C.: A multi-phase approach to biomedical event extraction. In: Proceedings of the Workshop on BioNL, BioNL 09, pp. 107–110 (2009)
Kim, J.-D., Sampo Pyysalo, T., Kim, M.-J., Lee, J.-Y., Hong, G., Rim, H.-C.: Overview of bionlp shared task 2011. In: Proceedings of BioNLP Shared Task 2011 Workshop, pp. 1–6, June 2011
Li, L., Yiwen Wang, D.H.: Improving feature-based biomedical event extraction system by integrating argument information. In: Proceedings of the BioNLP Shared Task 2013 Workshop, pp. 109–115, August 2013
Corinna Cortes, V.V.: Support-vector networks. In: Machine Learning, pp. 273–297 (1995)
Björne, J.: Biomedical event extraction with machine learning. Ph.D. thesis, University of Turku (2014)
Bjorne, J., Salakoski, T.: Generalizing biomedical event extraction. In: Proceedings of BioNLP Shared Task 2011 Workshop, June 2011
Jari Bjrne, T.S.: Tees 2.2: biomedical event extraction for diverse corpora. BMC Bioinform. 16, s4 (2015)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 181–197 (2002). Velingrad
David McClosky, M.S., Manning, C.D.: Event extraction as dependency parsing for BioNLP 2011. In: Proceedings of BioNLP Shared Task 2011 Workshop, pp. 41–45, June 2011
McClosky, D.: Any domain parsing: automatic domain adaptation for parsing. Ph.D. Thesis (2010)
Riedela, S., David McCloskyb, M., Surdeanu, M., McCallum, A., Manning, C.D.: Model combination for event extraction in BioNLP 2011. In: Proceedings of BioNLP Shared Task 2011 Workshop, pp. 51–55 (2011)
Riedel, S., McCallum, A.: Robust biomedical event extraction with dual decomposition and minimal domain adaptation. In: Proceedings of BioNLP Shared Task 2011 Workshop, pp. 46–50 (2011)
Miwa, M., Pyysalo, S., Ohta, T., Ananiadou, S.: Wide coverage biomedical event extraction using multiple partially overlapping corpora (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Majumder, A., Ekbal, A., Naskar, S.K. (2017). Feature Selection and Class-Weight Tuning Using Genetic Algorithm for Bio-molecular Event Extraction. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-59569-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59568-9
Online ISBN: 978-3-319-59569-6
eBook Packages: Computer ScienceComputer Science (R0)