Abstract
Previous research has shown that syntactic features are the most informative features in automatic verb classification. We investigate their optimal characteristics by comparing a range of feature sets extracted from data where the proportion of verbal arguments and adjuncts is controlled. The data are obtained from different versions of valex [1] – a large scf lexicon for English which was acquired automatically from several corpora and the Web. We evaluate the feature sets thoroughly using four supervised classifiers and one unsupervised method. The best performing feature set includes rich syntactic information about both arguments and adjuncts of verbs. When combined with our best performing classifier (a novel Gaussian classifier), it yields the promising accuracy of 64.2% in classifying 204 verbs to 17 Levin (1993) classes. We discuss the impact of our results on the state-or-art and propose avenues for future work.
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.
Similar content being viewed by others
References
Korhonen, A., Krymolowski, Y., Briscoe, T.: A large subcategorization lexicon for natural language processing applications. In: Proceedings of LREC (2006)
Merlo, P., Stevenson, S.: Automatic verb classification based on statistical distributions of argument structure. Computational Linguistics 27, 373–408 (2001)
Korhonen, A., Krymolowski, Y., Collier, N.: Automatic classification of verbs in biomedical texts. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual meeting of the ACL, pp. 345–352 (2006)
Schulte im Walde, S.: Experiments on the automatic induction of german semantic verb classes. Computational Linguistics 32, 159–194 (2006)
Joanis, E., Stevenson, S., James, D.: A general feature space for automatic verb classification. Natural Language Engineering (forthcoming, 2007)
Dorr, B.J.: Large-scale dictionary construction for foreign language tutoring and interlingual machine translation. Machine Translation 12, 271–322 (1997)
Prescher, D., Riezler, S., Rooth, M.: Using a probabilistic class-based lexicon for lexical ambiguity resolution. In: 18th International Conference on Computational Linguistics, Saarbrücken, Germany, pp. 649–655 (2000)
Swier, R., Stevenson, S.: Unsupervised semantic role labelling. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain, pp. 95–102 (2004)
Dang, H.T.: Investigations into the Role of Lexical Semantics in Word Sense Disambiguation. PhD thesis, CIS, University of Pennsylvania (2004)
Shi, L., Mihalcea, R.: 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 (2005)
Jackendoff, R.: Semantic Structures. MIT Press, Cambridge (1990)
Levin, B.: English Verb Classes and Alternations. Chicago University Press, Chicago (1993)
Miller, G.A.: WordNet: An on-line lexical database. International Journal of Lexicography 3, 235–312 (1990)
Schulte im Walde, S.: Clustering verbs semantically according to their alternation behaviour. In: Proceedings of COLING, Saarbrücken, Germany, pp. 747–753 (2000)
Kipper, K., Dang, H.T., Palmer, M.: Class-based construction of a verb lexicon. In: AAAI/IAAI, pp. 691–696 (2000)
Briscoe, E.J., Carroll, J.: Automatic extraction of subcategorization from corpora. In: Proceedings of the 5th ACL Conference on Applied Natural Language Processing, Washington DC, pp. 356–363 (1997)
Briscoe, E.J., Carroll, J.: Robust accurate statistical annotation of general text. In: Proceedings of the 3rd LREC, Las Palmas, Gran Canaria, pp. 1499–1504 (2002)
Boguraev, B., Briscoe, T.: Large lexicons for natural language processing: utilising the grammar coding system of ldoce. Comput. Linguist. 13, 203–218 (1987)
Grishman, R., Macleod, C., Meyers, A.: Comlex syntax: building a computational lexicon. In: Proceedings of the 15th conference on Computational linguistics, Morristown, NJ, USA, Association for Computational Linguistics, pp. 268–272 (1994)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995)
Chang, C., Lin, J.: LIBSVM: a library for support vector machines (2001)
Hsu, W., Chang, C., Lin, J.: A practical guide to support vector classification (2003)
Pietra, S.D., Pietra, J.D., Lafferty, J.D.: Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 380–393 (1997)
Zhang, L.: Maximum Entropy Modeling Toolkit for Python and C++ (2004)
Puzicha, J., Hofmann, T., Buhmann, J.M.: A theory of proximity-based clustering: structure detection by optimization. Pattern Recognition 33, 617–634 (2000)
Ando, R.K., Zhang, T.: A high-performance semi-supervised learning method for text chunking. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 1–9 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, L., Korhonen, A., Krymolowski, Y. (2008). Verb Class Discovery from Rich Syntactic Data. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2008. Lecture Notes in Computer Science, vol 4919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78135-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-78135-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78134-9
Online ISBN: 978-3-540-78135-6
eBook Packages: Computer ScienceComputer Science (R0)