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VerbNet class assignment as a WSD task

Authors:

Susan Windisch Brown,

Dmitriy Dligach,

Martha PalmerAuthors Info & Claims

IWCS '11: Proceedings of the Ninth International Conference on Computational Semantics

Pages 85 - 94

Published: 12 January 2011 Publication History

Abstract

The VerbNet lexical resource classifies English verbs based on semantic and syntactic regularities and has been used for numerous NLP tasks, most notably, semantic role labeling. Since, in addition to thematic roles, it also provides semantic predicates, it can serve as a foundation for further inferencing. Many verbs belong to multiple VerbNet classes, with each class membership corresponding roughly to a different sense of the verb. A VerbNet token classifier is essential for current applications using the resource and could provide the basis for a deep semantic parsing system, one that made full use of VerbNet's extensive syntactic and semantic information. We describe our VerbNet classifier, which uses rich syntactic and semantic features to label verb instances with their appropriate VerbNet class. It achieves an accuracy of 88.67% with multiclass verbs, which is a 49% error reduction over the most frequent class baseline.

References

[1]

Abend, Omri, Roy Reichart, and Ari Rappoport. 2008. A supervised algorithm for verb disambiguation into VerbNet classes. Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pp. 9--16. Manchester, August.

Digital Library

[2]

Bikel, Daniel M, Richard Schwartz, and Ralph M. Weischedel. 1999. An algorithm that learns what's in a name. Machine Learning, 34(1--3): 211--231. Special Issue on Natural Language Learning.

Digital Library

[3]

Bobrow, Danny, B. Cheslow, Cleo Condoravdi, Lauri Karttunen, Tracy H. King, Rowan Nairn, Valeria de Paiva, Charlotte Price, and Annie Zaenen. 2007. PARC's Bridge and Question Answering System. In Proceedings of the Grammar Engineering Across Frameworks (GEAF07) Workshop, pp. 46--66, CSLI Publications.

[4]

Chang, Chih-Chung, and Chih-Jen Lin. 2001. LIBSVM: A library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

[5]

Chen, Jinying, and Martha Palmer. 2005. Towards robust high performance word sense disambiguation of English verbs using rich linguistic features. In Proceedings of the 2nd International Joint Conference on Natural Language Processing. October 11--13, 2005, Jeju Island, Korea.

Digital Library

[6]

Dang, Hoa T., and Martha Palmer. 2002. Combining contextual features for word sense disambiguation. In Proceedings of the SIGLEX/SENSEVAL Workshop on WSD: Recent Successes and Future Directions, in Conjunction with ACL-02, Philadelphia.

Digital Library

[7]

Dligach, Dmitriy. 2010. High performance word sense disambiguation with less manual effort. Ph.D. diss. University of Colorado.

Digital Library

[8]

Dligach, Dmitriy, and Martha Palmer. 2008. Novel semantic features for word sense disambiguation. In Proceedings of ACL-08: HLT, Short Papers, pp. 29--32, Columbus, Ohio.

Digital Library

[9]

Federici, Stefano, Simonetta Montemagni, and Vito Pirrelli. 1999. SENSE: An analogy-based word sense disambiguation system. Natural Language Engineering, 5(2): 207--218.

Digital Library

[10]

Fellbaum, Christiane. 1998. WordNet: An Electronic Lexical Database. MTT Press, Cambridge, Mass.

[11]

Girju, Roxana, Dan Roth, and Mark Sammons. 2005. Token-level disambiguation of VerbNet classes. In The Proceedings of the Interdisciplinary Workshop on the Identification and Representation of Verb Features and Verb Classes, K. Erk, A. Melinger, and S. Schulte im Walde (eds.), Saarland University, Saarbrucken, Germany.

[12]

Hanks, Patrick. 1996. Contextual dependencies and lexical sets. The International Journal of Corpus Linguistics, 1(1).

[13]

Hensman, Svetlana, and John Dunnion. 2004. Automatically building conceptual graphs using VerbNet and WordNet. In Proceedings of the 3rd International Symposium on Information and Communication Technologies (ISICT), Las Vegas, NV.

Digital Library

[14]

Ide, Nancy, and Yorick Wilks. 2006. Making sense about sense. In Eneko Agirre and Philip Edmonds (eds.) Word Sense Disambiguation: Algorithms and Applications. Dordrecht, The Netherlands: Springer.

[15]

Kipper-Schuler, Karin. 2005. VerbNet: A broad-coverage, comprehensive verb lexicon. Ph.D. thesis, Computer and Information Science Dept, University of Pennsylvania, Philadelphia, June.

[16]

Levin, Beth. 1993. English Verb Classes and Alternations. Chicago: University of Chicago Press.

[17]

Loper, Edward, Szu ting Yi, and Martha Palmer. 2007. Combining lexical resources: Mapping between PropBank and VerbNet. In The Proceedings of the 7th International Workshop on Computational Semantics, Tilburg, the Netherlands.

[18]

Nivre, Joakim, Johann Hall, Jens Nilsson, et al. 2007. MaltParser: A language-independent system for data-driven dependency parsing. Natural Language Engineering 13(2): 95--135.

[19]

Palmer, Martha, Daniel Gildea, Nianwen Xue. 2010. Semantic Role Labeling (eBook), In Synthesis Lectures on Human Language Technologies, ed., Graeme Hirst. Morgan & Claypool.

Digital Library

[20]

Sanderson, Mark. 2000. Retrieving with good sense. Information Retrieval. 2(1): 49--69.

Digital Library

[21]

Shi, Lei, and Rada Mihalcea. 2005. Putting pieces together: Combining FrameNet, VerbNet and WordNet for robust semantic parsing. In Proceedings of the Sixth International Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, Mexico.

Digital Library

[22]

Swier, R., and S. Stevenson. 2004. Unsupervised semantic role labeling. In Proceedings of the 2004 Conf. on Empirical Methods in Natural Language Processing, pp. 95--102, Barcelona, Spain.

[23]

Zaenen, A., D. G. Bobrow, C. Condoravdi. 2008. The encoding of lexical implications in VerbNet: Predicates of change of locations. In LREC Proceedings {http://www.lrec-conf.org/proceedings/lrec2008/pdf/101_paper.pdf}

[24]

Zapirain, Beñat, Eneko Agirre, and Lluís Màrquez. 2008. Robustness and generalization of role sets: PropBank vs. VerbNet. In Proceedings of the 46th Annual Meeting of the Association of Computational Linguistics, ACL-08: HLT, Columbus, Ohio, pp. 550--558.

Cited By

Majewska OVulić IMccarthy DHuang YMurakami ALaippala VKorhonen A(2018)Investigating the cross-lingual translatability of VerbNet-style classificationLanguage Resources and Evaluation10.1007/s10579-017-9403-x52:3(771-799)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10579-017-9403-x
Scarton CSun LKipper-Schuler KDuran MPalmer MKorhonen A(2014)Verb Clustering for Brazilian PortugueseProceedings of the 15th International Conference on Computational Linguistics and Intelligent Text Processing - Volume 840310.1007/978-3-642-54906-9_3(25-39)Online publication date: 6-Apr-2014
https://dl.acm.org/doi/10.1007/978-3-642-54906-9_3
Goh HSoon LHaw S(2013)Automatic dominant character identification in fables based on verb analysis - Empirical study on the impact of anaphora resolutionKnowledge-Based Systems10.5555/2770961.277110954:C(147-162)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.5555/2770961.2771109
Show More Cited By

Index Terms

VerbNet class assignment as a WSD task
1. Applied computing
  1. Arts and humanities
    1. Language translation
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Published In

cover image DL Hosted proceedings

IWCS '11: Proceedings of the Ninth International Conference on Computational Semantics

January 2011

408 pages

Program Chairs:
Johan Bos
University of Groningen
,
Stephen Pulman
Oxford University

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 12 January 2011

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Cited By

Majewska OVulić IMccarthy DHuang YMurakami ALaippala VKorhonen A(2018)Investigating the cross-lingual translatability of VerbNet-style classificationLanguage Resources and Evaluation10.1007/s10579-017-9403-x52:3(771-799)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10579-017-9403-x
Scarton CSun LKipper-Schuler KDuran MPalmer MKorhonen A(2014)Verb Clustering for Brazilian PortugueseProceedings of the 15th International Conference on Computational Linguistics and Intelligent Text Processing - Volume 840310.1007/978-3-642-54906-9_3(25-39)Online publication date: 6-Apr-2014
https://dl.acm.org/doi/10.1007/978-3-642-54906-9_3
Goh HSoon LHaw S(2013)Automatic dominant character identification in fables based on verb analysis - Empirical study on the impact of anaphora resolutionKnowledge-Based Systems10.5555/2770961.277110954:C(147-162)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.5555/2770961.2771109
Croce DBasili RMoschitti APalmer MLi HLin COsborne M(2012)Verb classification using distributional similarity in syntactic and semantic structuresProceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 110.5555/2390524.2390562(263-272)Online publication date: 8-Jul-2012
https://dl.acm.org/doi/10.5555/2390524.2390562
Eckle-Kohler JGurevych IDaelemans W(2012)Subcat-LMFProceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics10.5555/2380816.2380882(550-560)Online publication date: 23-Apr-2012
https://dl.acm.org/doi/10.5555/2380816.2380882

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