Abstract
In this paper, a supervised learning method of semantic role labeling is presented. It is based on maximum entropy conditional probability models. This method acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed for a few words selected from sections of the Wall Street Journal part of the Penn Treebank corpus.
This paper has been partially supported by the Spanish Government (CICYT) under project number TIC2003-07158-C04-01.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carreras, X., Màrquez, L.: Introduction to the CoNLL 2004 Shared Task: Semantic Role Labelling. In: Proceedings of the Eighth Conference on Natural Language Learning (CoNLL 2004), Boston, MA, USA, Mayo (2004)
Chen, J., Rambow, O.: Use of deep linguistic features for the recognition and labeling of semantic arguments. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2003 (July 2003)
Fleischman, M., Kwon, N., Hovy, E.: Maximum Entropy Models for FrameNet Classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2003) (July 2003)
Gildea, D., Hockenmaier, J.: Identifying semantic roles using combinatory categorial grammar. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2003 (July 2003)
Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288 (2002)
Gildea, D., Palmer, M.: The necessity of parsing for predicate argument recognition. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistic (ACL), Philadelphia (Julio 2002)
Giménez, J., Màrquez, L.: Fast and Accurate Part-of-Speech Tagging: The SVM Approach Revisited. In: Proceedings of Recent Advances in Natural Language Processing 2003, Borovets, Bulgaria (Septiembre 2003)
Hacioglu, K., Ward, W.: Target word detection and semantic role chunking using support vector machines. In: Proceedings of the Human Language Technology Conference (HLT-NAACL), Edmonton, Canada (Junio 2003)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)
Marcus, M.P., Santorini, B., Marcinkiewicz, M.A.: Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics (19) (1993)
Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: An annotated corpus of semantic roles. Computational Linguistics (2004) (submitted)
Pradhan, S., Hacioglu, K., Krugler, V., Ward, W., Martin, J.H., Jurafsky, D.: Support vector learning for semantic argument classification. Technical report, International Computer Science Institute, Center for Spoken Language Research, University of Colorado (2003)
Pradhan, S., Hacioglu, K., Ward, W., Martin, J.H., Jurafsky, D.: Semantic role parsing: Adding semantic structure to unstructured text. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM), Melbourne, Florida, USA (Noviembre 2003)
Ratnaparkhi, A.: Maximum Entropy Models for Natural Language Ambiguity Resolution. Ph.D. thesis, University of Pennsylvania (1998)
Suárez, A., Palomar, M.: A maximum entropy-based word sense disambiguation system. In: Proceedings of the 19th International Conference on Computational Linguistics (COLING), Taipei, Taiwan, Agosto 2002, pp. 960–966 (2002)
Surdeanu, M., Harabagiu, S., Williams, J., Aarseth, P.: Using predicate-argument structures for information extraction. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL), Sapporo, Japan (July 2003)
Thompson, A., Levy, R., Manning, C.D.: A generative model for semantic role labeling. In: Proceedings of the 14th European Conference on Machine Learning (ECML), Cavtat-Dubrovnik, Croatia (September 2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moreda, P., Fernández, M., Palomar, M., Suárez, A. (2004). Identifying Semantic Roles Using Maximum Entropy Models. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2004. Lecture Notes in Computer Science(), vol 3206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30120-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-30120-2_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23049-6
Online ISBN: 978-3-540-30120-2
eBook Packages: Springer Book Archive