Skip to main content
Springer Nature Link
Log in

Comparing Two Approaches for the Recognition of Temporal Expressions

  • Conference paper
KI 2009: Advances in Artificial Intelligence (KI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5803))

Included in the following conference series:

Abstract

Temporal expressions are important structures in natural language. In order to understand text, temporal expressions have to be extracted and normalized. In this paper we present and compare two approaches for the automatic recognition of temporal expressions, based on a supervised machine learning approach and trained on TimeBank. The first approach performs a token-by-token classification and the second one does a binary constituent-based classification of chunk phrases. Our experiments demonstrate that on the TimeBank corpus constituent-based classification performs better than the token-based one. It achieves F1-measure values of 0.852 for the detection task and 0.828 when an exact match is required, which is better than the state-of-the-art results for temporal expression recognition on TimeBank.

This work has been partly funded by the Flemish government (through IWT) and by Space Applications Services NV as part of the ITEA2 project LINDO (ITEA2-06011).

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

Access this chapter

Log in via an institution

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. Pustejovsky, J., Castaño, J., Ingria, R., Saurí, R., Gaizauskas, R., Setzer, A., Katz, G.: TimeML: Robust Specification of Event and Temporal Expressions in Text. In: Fifth International Workshop on Computational Semantics (2003)

    Google Scholar 

  2. Pustejovsky, J., Hanks, P., Saurí, R., See, A., Day, D., Ferro, L., Gaizauskas, R., Lazo, M., Setzer, A., Sundheim, B.: The TimeBank Corpus. In: Corpus Linguistics 2003, pp. 647–656 (2003)

    Google Scholar 

  3. TERN, Evaluation Plan (2004), http://fofoca.mitre.org/tern_2004/tern_evalplan-2004.29apr04.pdf

  4. Boguraev, B., Ando, R.K.: TimeBank-Driven TimeML Analysis. In: Annotating, Extracting and Reasoning about Time and Events. Dagstuhl Seminar Proceedings. Dagstuhl, Germany (2005)

    Google Scholar 

  5. Ferro, L.: TERN Evaluation Task Overview and Corpus, http://fofoca.mitre.org/tern_2004/ferro1_TERN2004_task_corpus.pdf

  6. Ferro, L., Gerber, L., Mani, I., Sundheim, B., Wilson, G.: TIDES 2003, Standard for the Annotation of Temporal Expressions (2003), http://timex2.mitre.org

  7. Boguraev, B., Pustejovsky, J., Ando, R., Verhagen, M.: TimeBank Evolution as a Community Resource for TimeML Parsing. Language Resource and Evaluation 41(1), 91–115 (2007)

    Article  Google Scholar 

  8. Mani, I., Wilson, G.: Robust Temporal Processing of News. In: 38th Annual Meeting on Association for Computational Linguistics, pp. 69–76 (2000)

    Google Scholar 

  9. Negri, M., Marseglia, L.: Recognition and Normalization of Time Expressions: ITC-irst at TERN 2004. Technical Report, ITC-irst, Trento (2004)

    Google Scholar 

  10. Ahn, D., Adafre, S.F., de Rijke, M.: Extracting Temporal Information from Open Domain Text: A Comparative Exploration. Digital Information Management 3(1), 14–20 (2005)

    Google Scholar 

  11. Hacioglu, K., Chen, Y., Douglas, B.: Automatic Time Expression Labeling for English and Chinese Text. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 548–559. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  12. Poveda, J., Surdeanu, M., Turmo, J.: A Comparison of Statistical and Rule-Induction Learners for Automatic Tagging of Time Expressions in English. In: International Symposium on Temporal Representation and Reasoning, pp. 141–149 (2007)

    Google Scholar 

  13. Ahn, D., van Rantwijk, J., de Rijke, M.: A Cascaded Machine Learning Approach to Interpreting Temporal Expressions. In: NAACL-HLT 2007 (2007)

    Google Scholar 

  14. Ratnaparkhi, A.: A Maximum Entropy Model for Part-of-Speech Tagging. In: Conference on Empirical Methods in Natural Language Processing, pp. 133–142 (1996)

    Google Scholar 

  15. Ratnaparkhi, A.: Learning to Parse Natural Language with Maximum Entropy Models. Machine Learning 34(1), 151–175 (1999)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, K.U.Leuven, Celestijnenlaan 200A, Heverlee, B-3001, Belgium

    Oleksandr Kolomiyets & Marie-Francine Moens

Authors
  1. Oleksandr Kolomiyets
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marie-Francine Moens
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. GET Lab, University of Paderborn, Pohlweg 47-49, 33098, Paderborn, Germany

    Bärbel Mertsching , Marcus Hund  & Zaheer Aziz ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kolomiyets, O., Moens, MF. (2009). Comparing Two Approaches for the Recognition of Temporal Expressions. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04617-9_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04616-2

  • Online ISBN: 978-3-642-04617-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics