Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Graph-Based Semi-supervised Clustering for Semantic Classification of Unknown Words

  • Conference paper
Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 348))

  • 1164 Accesses

Abstract

This paper presents a method for semantic classification of unknown verbs including polysemies into Levin-style semantic classes. We propose a semi-supervised clustering, which is based on a graph-based unsupervised clustering technique. The algorithm detects the spin configuration that minimizes the energy of the spin glass. Comparing global and local minima of an energy function, called the Hamiltonian, allows for the detection of nodes with more than one cluster. We extended the algorithm so as to employ a small amount of labeled data to aid unsupervised learning, and applied the algorithm to cluster verbs including polysemies. The distributional similarity between verbs used to calculate the Hamiltonian is in the form of probability distributions over verb frames. The result obtained using 110 test polysemous verbs with labeled data of 10% showed 0.577 F-score.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning Distance Functions using Equivalence Relations. In: Proc. of the 20th International Conference on Machine Learning, pp. 11–18 (2003)

    Google Scholar 

  2. Bilenko, M., Basu, S., Mooney, R.J.: Integrating Constraints and Metric Learning in Semi-Supervised Clustering. In: Proc. of the 21st International Conference on Machine Learning, pp. 81–88 (2004)

    Google Scholar 

  3. Bouraev, B., Briscoe, E.J., Carroll, J., Carter, D., Grover, C.: The Derivation of a Grammatically-Indexed Lexicon from the Longman Dictionary of Contemporary English. In: Proc. of the 25th Annual Meeting of the Association for Computational Linguistics, pp. 193–200 (1987)

    Google Scholar 

  4. Brew, C., Walde, S.S.: Spectral Clustering for German Verbs. In: Proc. of 2002 Conference on Empirical Methods in Natural Language Processing, pp. 117–123 (2002)

    Google Scholar 

  5. Briscoe, E.J., Carroll, J.: Automatic Extraction of Subcategorization from Corpora. In: Proc. of 5th ACL Conference on Applied Natural Language Processing, pp. 356-363 (1997)

    Google Scholar 

  6. Briscoe, E.J., Carroll, J.: Robust Accurate Statistical Annotaion of General Text. In: Proc. of 3rd International Conference on Language Resources and Evaluation, pp. 1499-1504 (2002)

    Google Scholar 

  7. Chen, K.J., Chen, C.J.: Automatic Semantic Classification for Chinese Unknown Compound Nouns. In: Proc. of 38th Annual Meeting of the Association for Computational Linguistics, pp. 125–130 (2000)

    Google Scholar 

  8. Dagan, I., Lee, L., Pereira, F.C.N.: Similarity-based Models of Word Cooccurrence Probabilities. Machine Learning 34(1-3), 43–69 (1999)

    Article  MATH  Google Scholar 

  9. Dorr, B.: Large-scale Dictionary Construction for Foreign Language Tutoring and Interlingual Machine Translation. Machine Translation 128(4), 271–325 (1997)

    Article  Google Scholar 

  10. Galley, M., McKeown, K.: Improving Word Sense Disambiguation in Lexical Chaining. In: Proc. of 19th International Joint Conference on Artificial Intelligence, pp. 1486–1488 (2003)

    Google Scholar 

  11. Grishman, R., Macleod, C., Meyers, A.: Complex Syntax: Building a Computational Lexicon. In: Proc. of International Conference on Computational Linguistics, pp. 268–272 (1994)

    Google Scholar 

  12. Hindle, D.: Noun Classification from Predicate-Argument Structures. In: Proc. of 28th Annual Meeting of the Association for Computational Linguistics, pp. 268–275 (1990)

    Google Scholar 

  13. Hughes, J.: Automatically Acquiring Classification of Words, Ph.D. thesis University of Leeds (1994)

    Google Scholar 

  14. Jannink, J., Wiederhold, G.: Thesaurus Entry Extraction from an Online Dictionary. In: Proc. of Fusion 1999 (1999)

    Google Scholar 

  15. Kermanidis, K., Maragoudakis, M., Fakotakis, N., Kokkinakis, G.K.: Natural Language Engineering. Learning Verb Complements for Modern Greek: Balancing the Noisy Dataset 14(1), 71–100 (2008)

    Google Scholar 

  16. Kudo, T., Matsumoto, Y.: Fast Methods for Kernel-based Text Analysis. In: Proc. of 41st Annual Meeting of the Association for Computational Linguistics, pp. 24–31 (2003)

    Google Scholar 

  17. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  18. Korhonen, A.: Subcategorization Acquisition, Ph.D. thesis University of Cambridge (2002)

    Google Scholar 

  19. Korhonen, A., Krymolowski, Y., Marx, Z.: Clustering Polysemic Subcategorization Frame Distributions Semantically. In: Proc. of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 64–71 (2003)

    Google Scholar 

  20. Korhonen, A., Krymolowski, Y., Briscoe, T.: A Large Subcategorization Lexicon for Natural Language Processing Applications. In: Proc. of the 5th International Conference on Language Resources and Evaluation (2006)

    Google Scholar 

  21. Lee, L.: Measures of Distributional Similarity. In: Proc. of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 25–32 (1999)

    Google Scholar 

  22. Leech, G.: 100 Million Words of English: The British National Corpus. Language Research 28(1), 1–13 (1992)

    MathSciNet  Google Scholar 

  23. Levin, B.: English Verb Classes and Alternations. Chicago University Press (1993)

    Google Scholar 

  24. Lin, D.: Automatic Retrieval and Clustering of Similar Words. In: Proc. of 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, pp. 768–773 (1998)

    Google Scholar 

  25. Matsuo, Y., Sakaki, T., Uchiyama, K., Ishizuka, M.: Graph-based Word Clustering using a Web Search Engine. In: Proc. of 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pp. 542–550 (2006)

    Google Scholar 

  26. Mihalcea, R.: Unsupervised Large Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling. In: Proc. of the Human Language Technology/Empirical Methods in Natural Language Processing Conference, pp. 411–418 (2005)

    Google Scholar 

  27. Muller, P., Hathout, N., Gaume, B.: Synonym Extraction Using a Semantic Distance on a Dictionary. In: Proc. of the Workshop on TextGraphs, pp. 65–72 (2006)

    Google Scholar 

  28. Navigli, R.: A Structural Approach to the Automatic Adjudication of Word Sense Disagreements. Natural Language Engineering 14(4), 547–573 (2008)

    Article  Google Scholar 

  29. Navigli, R.: Word Sense Disambiguation: A Survey. ACM Computing Surveys 41(2), 1–69 (2009)

    Article  Google Scholar 

  30. Ng, A.Y., Jordan, M.I., Weiss, Y.: On Spectral Clustering: Analysis and an Algorithm. In: Advances in Neural Information Processing Systems 14. MIT Press (2002)

    Google Scholar 

  31. Pereira, F., Tishby, N., Lee, L.: Distributional Clustering of English Words. In: Proc. of the 31st Annual Meeting of the Association for Computational Linguistics, pp. 183–190 (1993)

    Google Scholar 

  32. Reichardt, J., Bornholdt, S.: Detecting Fuzzy Community Structure in Complex Networks with a Potts Model. Physical Review Letters 93(21) (2004)

    Google Scholar 

  33. Reichardt, J., Bornholdt, S.: Statistical Mechanics of Community Detection. Physical Review E 74 (2006)

    Google Scholar 

  34. Reiter, E., Dale, R.: Building Natural Language Generation Systems. Cambridge University Press (2000)

    Google Scholar 

  35. Resnik, P.: Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In: Proc. of 14th International Joint Conference on Artificial Intelligence, pp. 448–453 (1995)

    Google Scholar 

  36. Rooth, M.: Two-Dimensional Clusters in Grammatical Relations. In: Inducing Lexicons with the EM Algorithm, AIMS Report 4(3) (1998)

    Google Scholar 

  37. Rooth, M., Riezler, S., Prescher, D., Carroll, G., Beil, F.: Inducing a Semantically Annotated Lexicon via EM-Based Clustering. In: Proc. of the 37th Annual Meeting of the Association for Computational Linguistics (1999)

    Google Scholar 

  38. Schulte im Walde, S.: Clustering Verbs Semantically according to their Alternation Behaviour. In: Proc. of the 18th International Conference on Computational Linguistics, pp. 747–753 (2000)

    Google Scholar 

  39. Schulte im Walde, S., Brew, C.: Inducing German Semantic Verb Classes from Purely Syntactic Subcategorisation Information. In: Proc. of the 40th Annual Meeting of the Association for Computational Linguistics (2002)

    Google Scholar 

  40. Schulte im Walde, S.: Experiments on the Automatic Induction of German Semantic Verb Classes. Computational Linguistics 32(2), 159–194 (2006)

    Article  Google Scholar 

  41. Schulte im Walde, S., Hying, C., Scheible, C., Schmid, H.: Combining EM Training and the MDL Principle for an Automatic Verb Classification Incorporating Selectional Preferences. In: Proc. of the 46th Annual Meeting of the Association for Computational Linguistics, pp. 496–504 (2008)

    Google Scholar 

  42. Sinha, R., Mihalcea, R.: Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity. In: Proc. of the IEEE International Conference on Semantic Computing, pp. 46–54 (2007)

    Google Scholar 

  43. Stevenson, S., Joanis, E.: Semi-Supervised Verb-Class Discovery using Noisy Features. In: Proc. of the 7th Conference on Natural Language Learning at HLT-NAACL 2003, pp. 71–78 (2003)

    Google Scholar 

  44. Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained K-Means Clustering with Background Knowledge. In: Proc. of 18th International Conference on Machine Learning, pp. 577–584 (2001)

    Google Scholar 

  45. D. Widdows and B. Dorow, A Graph Model for Unsupervised Lexical Acquisition. In: Proc. of 19th International Conference on Computational Linguistics (COLING 2002), pp. 1093-1099, (2002)

    Google Scholar 

  46. Witten, I.H., Bell, T.C.: The Zero-Frequency Problem: Estimating the Probabilities of Novel Events in Adaptive Text Compression. IEEE Transactions on Information Theory 37(4), 1085–1094 (1991)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Japan

    Fumiyo Fukumoto & Yoshimi Suzuki

Authors
  1. Fumiyo Fukumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshimi Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IST - Technical University of Lisbon, Av.Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Ana Fred

  2. Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

    Jan L. G. Dietz

  3. Informatics Research Centre, Henley Business School, University of Reading, RG6 6UD, Reading, UK

    Kecheng Liu

  4. INSTICC and IPS, Estefanilha, Setúbal, Portugal

    Joaquim Filipe

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fukumoto, F., Suzuki, Y. (2013). Graph-Based Semi-supervised Clustering for Semantic Classification of Unknown Words. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2011. Communications in Computer and Information Science, vol 348. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37186-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37186-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37185-1

  • Online ISBN: 978-3-642-37186-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation