Skip to main content

Improving Metadata by Filtering Contextual Semantic Role Information

  • Conference paper
  • 1084 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 240))

Abstract

This paper proposes a method to automatically improve a web page’s metadata using the semantic content of the page. Thus, using a semantic role labeling system, the web page content is parsed and the entities that frequently play core semantic roles are considered for addition to the web page’s list of metadata. Semantic role analysis answers questions such as: “What role has an entity in a specific context?” or “When, why, where or how an event takes place?”.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baker, G., Collin, F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: Proceedings of the COLING-ACL, Montreal, Canada (1998)

    Google Scholar 

  2. Chen, J., Rambow, O.: Use of deep linguistic features for the recognition and labeling of semantic arguments. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (2003)

    Google Scholar 

  3. Fillmore Charles, J.: The case for case. In: Bach, Harms (eds.) Universals in Linguistic Theory, pp. 1–88. Holt, Rinehart, and Winston, New York (1968)

    Google Scholar 

  4. Fillmore Charles, J.: Frame semantics. In: Linguistics in the Morning Calm, pp. 111–137. Hanshin Publishing, Seoul (1982)

    Google Scholar 

  5. Daniel, G., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288 (2002)

    Article  Google Scholar 

  6. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)

    Google Scholar 

  7. Levin, B., Rappaport Hovav, M.: Argument Realization. Research Surveys in Linguistics Series. Cambridge University Press, Cambridge (2005)

    Book  Google Scholar 

  8. Lluis, M., Carreras, X., Litkowski, K.C., Stevenson, S.: Semantic role labeling: An introduction to the Special Issue. Computational Linguistics 34(2), 145–159 (2008)

    Article  Google Scholar 

  9. Roser, M., Daelemans, W., Van Asch, V.: A combined memory-based semantic role labeler of English. In: Proceedings of the Twelfth Conference on Computational Natural Language Learning, Manchester, UK, pp. 208–212 (2008)

    Google Scholar 

  10. Ochoa, X., Duval, E.: Times of Convergence. Technologies Across Learning Context, pp. 322–325 (2008)

    Google Scholar 

  11. Martha, P., Gildea, D., Kingsbury, P.: The proposition bank: An annotated corpus of semantic roles. Computational Linguistics 31(1), 71–106 (2005)

    Article  Google Scholar 

  12. Sameer, P., Hacioglu, K., Krugler, V., Ward, W., Martin, J.H., Jurafsky, D.: Support vector learning for semantic argument classification. Machine Learning Journal 60(13), 11–39 (2005)

    Google Scholar 

  13. Sah, M., Wade, V.: Automatic metadata extraction from multilingual enterprise content. In: Proc. of the 19th ACM International Conference on Information and Knowledge Management (CIKM 2010), pp. 1665–1668. ACM, New York (2010)

    Google Scholar 

  14. Surdeanu, M., Harabagiu, S., Williams, J., Aarseth, P.: Using predicate-argument structures for information extraction. In: Proceedings of the 41th Annual Meeting of the Association for Computational Linguistics, Tokyo, pp. 8–15 (2003)

    Google Scholar 

  15. Trandabăţ, D.: Natural Language Processing Using Semantic Frames, PhD Thesis, University Al. I. Cuza Iasi, Romania

    Google Scholar 

  16. Trandabăţ, D.: Towards automatic cross-lingual transfer of semantic annotation. In: 6e Rencontres Jeunes Chercheurs en Recherche d’Information (RJCRI) 2011, Avignon, France, March 16-18 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Trandabăţ, D. (2011). Improving Metadata by Filtering Contextual Semantic Role Information. In: García-Barriocanal, E., Cebeci, Z., Okur, M.C., Öztürk, A. (eds) Metadata and Semantic Research. MTSR 2011. Communications in Computer and Information Science, vol 240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24731-6_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24731-6_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24730-9

  • Online ISBN: 978-3-642-24731-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics