ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

A Framework for Semantic Interpretation of Noun Compounds Using Tratz Model and Binary Features

  • Received : 2011.10.23
  • Accepted : 2012.07.05
  • Published : 2012.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Na$\ddot{i}$ve Bayes, and a decision tree, is also shown.

Keywords

References

  1. S.N. Kim and T. Baldwin, "Benchmarking Noun Compound Interpretation," Joint Conf. Natural Language Process. (IJCNLP), Hyderabad, India, 2008.
  2. D. Ó Séaghdha and A. Copestake, "Semantic Classification with Distributional Kernels," COLING, 2008.
  3. D. O Seaghdha, Learning Compound Noun Semantics, doctoral dissertation, Computer Laboratory, University of Cambridge, Cambridge, England, UK, 2008.
  4. S. Tratz and E. Hovy, "A Taxonomy, Dataset, and Classifier for Automatic Noun Compound Interpretation," Proc. 48th Annual Meeting Assoc. Computational Linguistics (ACL-10), Uppsala, Sweden, 2010, pp. 678-687.
  5. D. Ó Séaghdha and A. Copestake, "Co-occurrence Contexts for Noun Compound Interpretation," Proc. ACL-07 Workshop Broader Perspective Multiword Expressions, Prague, Czech Republic, 2007.
  6. S.N. Kim and T. Baldwin, "An Unsupervised Approach to Interpreting Noun Compounds," Proc. IEEE Int. Conf. Natural Language Process. Knowl. Eng. (NLP-KE-08), Beijing, China, 2008, pp. 1-7.
  7. D. O Seaghdha, "Semantic Classification with WordNet Kernels," Proc. HLT-NAACL, Boulder, Colorado, USA, 2009, pp. 237-240.
  8. B. Rosario and M. Hearst, "Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy," Proc. Conf. Empirical Methods Natural Language Process., Somerset, NJ, USA, 2001, pp. 82-90.
  9. D. Moldovan et al., "Models for the Semantic Classification of Noun Phrases," Proc. HLT-NAACL Workshop Computational Lexical Semantics, Boston, MA, USA, 2004, pp. 60-67.
  10. S.N. Kim and T. Baldwin, "Automatic Interpretation of Noun Compounds Using WordNet Similarity," Proc. 2nd Int. Joint Conf. Natural Language Process., 2005, pp. 945-956.
  11. C. Fellbaum, WordNet: An Electronic Lexical Database, Cambridge, MA: MIT Press, 1998.
  12. V. Nastase et al., "Learning Noun-Modifier Semantic Relations with Corpus-Based and WordNet-Based Features," Proc. AAAI, 2006, pp. 781-786.
  13. R. Girju, "Improving the Interpretation of Noun Phrases with Cross-Linguistic Information," Proc. ACL, 2007, pp. 568-575.
  14. S.N. Kim and T. Baldwin, "Interpreting Noun Compound Using Bootstrapping and Sense Collocation," Proc. Pacific Association Computational Linguistics (PACLING), 2007, pp. 129-136.
  15. J.-X. Huang et al., "Feature-Based Relation Classification Using Quantified Relatedness Information," ETRI J., vol. 32, no. 3, June 2010, pp. 482-485. https://doi.org/10.4218/etrij.10.0209.0353
  16. D. O Seaghdha and A.A. Copestake, "Using Lexical and Relational Similarity to Classify Semantic Relations," Proc. EACL, 2009, pp. 621-629.
  17. L. Burnard, Ed., Users Reference Guide for the British National Corpus, Oxford: Oxford University Press [for BNC Consortium], May 1995.
  18. T. Brants and A. Franz, "Web 1T 5-Gram Version 1," Philadelphia Linguistic Data Consortium, Philadelphia, PA, USA, 2006.
  19. K. Betty, Roget's Thesaurus of English Words and Phrases, UK: Penguin, 1998.
  20. S.N. Kim and T. Baldwin, "Disambiguating Noun Compounds," Proc. 22nd Conf. Artificial Intell., 2007, pp. 901-906.
  21. The Stanford Natural Language Processing Group, "Stanford Log-Linear Part-of-Speech Tagger," accessed 20 Sept. 2010. Available: http://nlp.stanford.edu/software/tagger.shtml
  22. G. Pirró and N. Seco, "Design, Implementation and Evaluation of a New Semantic Similarity Metric Combining Features and Intrinsic Information Content," On the Move to Meaningful Internet Systems: OTM 2008, R. Meersman and Z. Tari, Eds., Berlin/Heidelberg: Springer, vol. 5332, 2008, pp. 1271-1288.
  23. M. Jarmasz, Roget's Thesaurus as a Lexical Resource for Natural Language Processing, master's thesis, University of Ottawa, Ottawa, Ontario, Canada, 2003.
  24. G. Forman et al., "An Extensive Empirical Study of Feature Selection Metrics for Text Classification," J. Machine Learning Research, vol. 3, 2003, pp. 1289-1305.
  25. A.K. McCallum, "MALLET: A Machine Learning for Language Toolkit," 2002.M. Hall et al., "The WEKA Data Mining Software: An Update," SIGKDD Explor. Newsl., vol. 11, 2009, pp. 10-18. https://doi.org/10.1145/1656274.1656278
  26. I. Rish, "An Empirical Study of the Naive Bayes Classifier," IJCAI-01 Workshop on "Empirical Method in AI," 2001, pp. 41-46.
  27. D. Fradkin and I. Muchnik, "Support Vector Machines for Classification," Discrete Methods Epidemiology, vol. 70, 2006, pp. 13-20.
  28. A.L. Berger et al., "A Maximum Entropy Approach to Natural Language Processing," Computational Linguistics, vol. 22, 1996, pp. 39-71.
  29. L. Liu and M. Ozsu, Eds., Encyclopedia of Database Systems, Berlin/Heidelberg: Springer-Verlag, 2009.
  30. C. Chang and C. Lin, "LIBSVM: A Library for Support Vector Machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, May 2011.
  31. C. Hsu et al., "A Practical Guide to Support Vector Classification," Department of Computer Science, National Taiwan University, 2003. Available: http://www.csie.ntu.edu.tw/-cjlin/papers/guide/guide.pdf

Cited by

  1. Impact of Tweets on Box Office Revenue: Focusing on When Tweets are Written vol.36, pp.4, 2012, https://doi.org/10.4218/etrij.14.0113.0732
  2. Classifying Biomedical Literature Providing Protein Function Evidence vol.37, pp.4, 2012, https://doi.org/10.4218/etrij.15.0114.0041