A Comparison of Word Similarity Measures for Noun Compound Disambiguation

  • Paul Nulty
  • Fintan Costello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6206)


Noun compounds occur frequently in many languages, and the problem of semantic disambiguation of these phrases has many potential applications in natural language processing and other areas. One very common approach to this problem is to define a set of semantic relations which capture the interaction between the modifier and the head noun, and then attempt to assign one of these semantic relations to each compound. For example, the compound phrase flu virus could be assigned the semantic relation causal (the virus causes the flu); the relation for desert wind could be location (the wind is located in the desert). In this paper we investigate methods for learning the correct semantic relation for a given noun compound by comparing the new compound to a training set of hand-tagged instances, using the similarity of the words in each compound. The main contribution of this paper is to directly compare distributional and knowledge-based word similarity measures for this task, using various datasets and corpora. We find that the knowledge based system provides a much better performance when adequate training data is available.


noun compounds word similarity semantic classification disambiguation 


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  1. 1.
    Johnston, M., Busa, F.: Qualia structure and the compositional interpretation of compound. In: Proceedings of the ACL SIGLEX Workshop on Breadth and Depth of Semantic Lexicons (1996)Google Scholar
  2. 2.
    Levi, J.: The Syntax and Semantics of Complex Nominals. Academic Press, New York (1978)Google Scholar
  3. 3.
    Turney, P.D., Waterman, M.S.: Similarity of Semantic Relations. Computational Linguistics 32(3), 379–416 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Hearst, M.A.: Automatic Acquisition of Hyponyms from Large Text Corpor. In: Proceedings of Conf. Computational Linguistics (COLING 1992) (1992)Google Scholar
  5. 5.
    Seaghdha, O’.D., Copestake, A.: Using Lexical and Relational Similarity to Classify Semantic Relations. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2009), Athens, Greece (2009)Google Scholar
  6. 6.
    Nakov, P., Heast, M.: Solving Relational Similarity Problems using the Web as a Corpus. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL 2008), Columbus, OH (2008)Google Scholar
  7. 7.
    Seco, N., Veale, T., Hayes, J.: An Intrinsic Information Content Metric for Semantic Similarity in WordNet. In: The proceedings of ECAI 2004, the 16th European Conference on Artificial Intelligence, Valencia, Spain. John Wiley, Chichester (2004)Google Scholar
  8. 8.
    Nastase, V., Szpakowicz, S.: Exploring noun-modifier semantic relations. In: Proceedings of the 5th International Workshop on Computational Semantics (2003)Google Scholar
  9. 9.
    Seaghdha, O’.M.: Annotating and Learning Compound Noun Semantics. In: Proceedings of the ACL 2007 Student Research Workshop, Prague, Czech Republic (2007)Google Scholar
  10. 10.
    Kolhatkar, V., Pedersen, T.: WordNet::SenseRelate:: AllWords - A Broad Coverage Word Sense Tagger that Maximimizes Semantic Relatedness. In: The Proceedings of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies 2009 Conference, Boulder, CO., June 1-3 (2009)Google Scholar
  11. 11.
    Kilgarriff, A., Rychly, P., Smrz, P., Tugwell, D.: The Sketch Engine. In: Proc. of EURALEX 2004, pp. 105–116 (2004)Google Scholar
  12. 12.
    Bird, S., Loper, E.: NLTK: The Natural Language Toolki. In: Proceedings of the 42nd meeting o the ACL (Demonstration session) (2004)Google Scholar
  13. 13.
    Kim, S.N., Baldwin, T.: Automatic interpretation of noun compounds using WordNet similarity. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 945–956. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Lin, D.: An Information-Theoretic Definition of Similarity. In: Proceedings of the 15th International Conference on Machine Learning, Madson, WI (1998)Google Scholar
  15. 15.
    Ferraresi, A., Zanchetta, E., Bernardini, S., Baroni, M.: Introducing and evaluating ukWaC, a very large web-derived corpus of English. In: Proceedings of 4th WAC workshop, LREC, Marrakech, Morocco (2008)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Paul Nulty
    • 1
  • Fintan Costello
    • 1
  1. 1.School of Computer Science and InformaticsUniversity College DublinDublinIreland

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