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Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features

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Natural Language Processing – IJCNLP 2005 (IJCNLP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3651))

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Abstract

This paper shows that our WSD system using rich linguistic features achieved high accuracy in the classification of English SENSEVAL2 verbs for both fine-grained (64.6%) and coarse-grained (73.7%) senses. We describe three specific enhancements to our treatment of rich linguistic features and present their separate and combined contributions to our system’s performance. Further experiments showed that our system had robust performance on test data without high quality rich features.

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References

  1. Sanderson, M.: Word sense disambiguation and information retrieval. In: Proceedings of the 17th Int. ACM SIGIR, Dublin, IE (1994)

    Google Scholar 

  2. Stokoe, C., Oakes, M.P., Tait, J.: Word sense disambiguation and information retrieval revisited. In: Proceedings of the 26th annual int. ACM SIGIR conference on research and development in information retrieval, Toronto, Canada (2003)

    Google Scholar 

  3. Edmonds, P., Cotton, S.: SENSEVAL-2: Overview. In: Proceedings of SENSEVAL-2: 2nd Int. Workshop on Evaluating WSD Systems. ACL-SIGLEX, Toulouse, France (2001)

    Google Scholar 

  4. Yarowsky, D., Cucerzan, S., Florian, R., Schafer, C., Wicentowski, R.: The Johns hopkins SENSEVAL2 system description. In: Proceedings of SENSEVAL-2: 2nd Int.Workshop on Evaluating WSD Systems, Toulouse France (2001)

    Google Scholar 

  5. Dang, H.T., Palmer, M.: 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-2002, Philadelphia (2002)

    Google Scholar 

  6. David, M., Enek, A., Liuis, M.: Syntactic Features for High Precision Word Sense Disambiguation. In: Proceedings of the 19th International COLING, Taipei (2002)

    Google Scholar 

  7. Lin, D.: Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity. In: Proceedings of ACL-1997, Madrid, Spain (1997)

    Google Scholar 

  8. Lee, Y.K., Ng, H.T.: An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 41–48 (2002)

    Google Scholar 

  9. Mihalcea, R., Faruque, E.: Sense Learner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text. In: Proceedings of SENSEVAL-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain (2004)

    Google Scholar 

  10. Dang, H.T.: Investigations into the role of lexical semantics in word sense disambiguation. PhD Thesis. University of Pennsylvania (2004)

    Google Scholar 

  11. Fellbaum, C.: WordNet - an Electronic Lexical Database. The MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  12. McCallum, A.K.: MALLET: A Machine Learning for Language Toolkit (2002), http://www.cs.umass.edu/~mccallum/mallet

  13. Berger, A.L., Della Piertra, S.A., Della Pietra, V.J.: A maximum entropy approach to natural language processing. Compuational Linguistics 22(1), 39–71 (1996)

    Google Scholar 

  14. Chen, S.F., Rosenfeld, R.: A Gaussian prior for smoothing maximum entropy models. Technical Report CMU-CS-99-108, CMU (1999)

    Google Scholar 

  15. Bikel, D.M., Schwartz, R., Weischedel, R.M.: An algorithm that learns what’s in a name. Machine Learning, Special Issue on Natural Language Learning 34(1-3) (1999)

    Google Scholar 

  16. Lappin, S., Leass, H.: An algorithm for pronominal anaphora resolution. Computational Linguistics 20(4), 535–561 (1994)

    Google Scholar 

  17. Kingsbury, P., Palmer, M., Marcus, M.: Adding semantic annotation to the Penn Tree-Bank. In: Proceedings of HLT 2002, San Diego, CA (2002)

    Google Scholar 

  18. Ratnaparkhi, A.: Maximum entropy models for natural language ambiguity resolution. Ph.D. thesis, University of Pennsylvania (1998)

    Google Scholar 

  19. Bikel, D.M.: Design of a multi-lingual, parallel-processing statistical parsing engine. In: Proceedings of HLT 2002. San Diego, CA (2002)

    Google Scholar 

  20. Buitelaar, P.: Reducing lexical semantic complexity with systematic polysemous classes and underspecification. In: Poceedings of the ANLP Workshop on Syntactic and Semantic Complexity in NLP Systems, Seattle, WA (2000)

    Google Scholar 

  21. Palmer, M., Malaya, O.B., Dang, H.T.: Different sense granularities for different appli-cations. In: Proceedings of HLT/NAACL-2004, Boston (2004)

    Google Scholar 

  22. Marcus, M., Kim, G., Marcinkiewicz, M.A., MacIntyre, R., Ferguson, M., Katz, K., Schasberger, B.: The Penn Treebank: annotating predicate argument structure. In: Proceedings of the ARPA 1994 HLT Workshop (1994)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Jinying Chen & Martha Palmer

Authors
  1. Jinying Chen
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  2. Martha Palmer
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Editor information

Editors and Affiliations

  1. Center for Language Technology, Macquarie University, 2019, Sydney, NSW, Australia

    Robert Dale

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Kam-Fai Wong

  3. Institute for Infocomm Research, 21, Heng Mui Keng Terrace, 119613, Singapore

    Jian Su

  4. Language Information Sciences Research Centre, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong

    Oi Yee Kwong

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© 2005 Springer-Verlag Berlin Heidelberg

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Chen, J., Palmer, M. (2005). Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_81

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  • DOI: https://doi.org/10.1007/11562214_81

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29172-5

  • Online ISBN: 978-3-540-31724-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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