Skip to main content
SpringerLink
Log in
Book cover

Australasian Joint Conference on Artificial Intelligence

AI 2010: AI 2010: Advances in Artificial Intelligence pp 184–193Cite as

Learning Parse-Free Event-Based Features for Textual Entailment Recognition

  • Conference paper

  • 1802 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6464))

Abstract

We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these features can improve the effectiveness of the identification of entailment and no-entailment relationships.

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

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Rodrigo, A., Penas, A., Verdejo, F.: Towards an entity-based recognition of textual entailment. In: Proceedings of the Fourth PASCAL Challenges Workshop on Recognizing Textual Entailment, Gaithersburg, USA (2008)

    Google Scholar 

  2. Ageno, A., Farwell, D., Ferres, D., Cruz, F., Rodriguez, H.: TALP at TAC 2008: A semantic approach to recognizing textual entailment. In: Proceedings of the Fourth PASCAL Challenges Workshop on Recognizing Textual Entailment, Gaithersburg, USA (2008)

    Google Scholar 

  3. Burchardt, A., Pennacchiotti, M.: FATE: a FrameNet-Annotated Corpus for Textual Entailment. In: Proceedings of the Sixth International Language Resources and Evaluation (LREC 2008), Marrakech, Morocco (2008)

    Google Scholar 

  4. Burchardt, A., Pennacchiotti, M., Thater, S., Pinkal, M.: Assessing the impact of frame semantics on textual entailment. Natural Language Engineering 15(4), 527–550 (2009)

    Article  Google Scholar 

  5. Hickl, A., Bensley, J., Williams, J., Roberts, K., Rink, B., Shi, Y.: Recognizing textual entailment with LCC’s GROUNDHOG system. In: Proceedings of the Second PASCAL Challenges Workshop on Recognizing Textual Entailment, Venice, Italy, pp. 80–85 (2006)

    Google Scholar 

  6. Ofoghi, B., Yearwood, J., Ma, L.: The impact of frame semantic annotation, frame alignment techniques, and fusion methods on factoid answer processing. Journal of the American Society for Information Science and Technology (JASIST) 60(2), 247–263 (2009)

    Article  Google Scholar 

  7. Mac Cartney, B., Grenager, T., de Marneffe, M.-C., Cer, D., Manning, C.D.: Learning to recognize features of valid textual entailments. In: Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, New York City (2006)

    Google Scholar 

  8. Fillmore, C.J.: Frame semantics and the nature of language. In: Proceedings of the Annals of the New York Academy of Sciences: Conference on the Origin and Development of Language and Speech, pp. 20–32 (1976)

    Google Scholar 

  9. Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: Proceedings of the 17th International Conference on Computational Linguistics (COLING), pp. 86–90. Universite de Montreal, Montreal (1998)

    Chapter  Google Scholar 

  10. Corley, C., Mihalcea, R.: Measuring the semantic similarity of texts. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, MI (2005)

    Google Scholar 

  11. Sleator, D., Temperley, D.: Parsing English with a link grammar. In: Proceedings of the Third International Workshop on Parsing Technologies (1993)

    Google Scholar 

  12. Lin, D., Pantel, P.: DIRT - Discovery of inference rules from text. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, pp. 323–328 (2001)

    Google Scholar 

  13. Akhmatova, E., Molla, D.: Recognizing textual entailment via atomic propositions. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 385–403. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  14. Zanzotto, F.M., Moschitti, A.: Automatic learning of textual entailments with cross-pair similarities. In: Proceedings of the 21st COLING and 44th ACL, Sydney, Australia (2006)

    Google Scholar 

  15. Zanzotto, F.M., Pennacchiotti, M., Moschitti, A.: A machine learning approach to textual entailment recognition. Natural Language Engineering 15(4), 551–582 (2009)

    Article  Google Scholar 

  16. Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the Conference on New Methods in Language Processing, Manchester, UK (1994)

    Google Scholar 

  17. Lowe, J.B., Baker, C.F., Fillmore, C.J.: A frame-semantic approach to semantic annotation. In: Proceedings of the SIGLEX Workshop on Tagging Text with Lexical Semantics: Why, What, and How? (1997)

    Google Scholar 

  18. Ruppenhofer, J., Ellsworth, M., Petruck, M.R.L., Johnson, C.R.: FrameNet: Theory and practice (2005)

    Google Scholar 

  19. Erk, K., Pado, S.: Shalmaneser – A toolchain for shallow semantic parsing. In: Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC 2006), Genoa, Italy (2006)

    Google Scholar 

  20. Tatu, M., Moldovan, D.: A semantic approach to recognizing textual entailment. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT-EMNLP), Vancouver, British Columbia, Canada, pp. 371–378 (2005)

    Google Scholar 

  21. Yatbaz, M.A.: RTE4: Normalized dependency tree alignment using unsupervised n-gram word similarity score. In: Proceedings of the Fourth PASCAL Challenges Workshop on Recognizing Textual Entailment, Gaithersburg, USA (2008)

    Google Scholar 

  22. Kouylekov, M., Magnini, B.: Recognizing textual entailment with tree edit distance algorithms. In: Proceedings of the First PASCAL Challenges Workshop on Recognizing Textual Entailment, Southampton, UK, pp. 17–20 (2005)

    Google Scholar 

  23. Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M.: Textual entailment as syntactic graph distance: A rule based and a SVM based approach. In: Proceedings of the First PASCAL Challenges Workshop on Recognizing Textual Entailment, Southampton, UK, pp. 25–28 (2005)

    Google Scholar 

  24. Clark, P., Harrison, P.: Recognizing textual entailment with logic inference. In: Proceedings of the Fourth PASCAL Challenges Workshop on Recognizing Textual Entailment, Gaithersburg, USA (2008)

    Google Scholar 

  25. de Salvo Braz, R., Girju, R., Punyakanok, V., Roth, D., Sammons, M.: Textual entailment recognition based on dependency analysis and WordNet. In: Proceedings of the First PASCAL Challenges Workshop on Recognizing Textual Entailment, Southampton, UK, pp. 29–32 (2005)

    Google Scholar 

  26. Bosma, W.E., Callison-Burch, C.: Paraphrase substitution for recognizing textual entailment. In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol. 4730, pp. 1–8. Springer, Heidelberg (2006)

    Google Scholar 

  27. Burchardt, A., Frank, A.: Approaching Textual Entailment with LFG and FrameNet Frames. In: Proceedings of the Second PASCAL Challenges Workshop on Recognizing Textual Entailment, Venice, Italy (2006)

    Google Scholar 

  28. Ofoghi, B., Yearwood, J.: UB.dmirg: A syntactic lexical system for recognizing textual entailments. In: Proceedings of the Second PASCAL Challenges Workshop on Recognizing Textual Entailment, Gaithersburg, USA (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Informatics and Applied Optimization, University of Ballarat, P.O. Box 663, Ballarat, Victoria, 3350, Australia

    Bahadorreza Ofoghi & John Yearwood

  2. Institute of Sport, Exercise, and Active Living, Victoria University, P.O. Box 14428, Melbourne, Victoria, 8001, Australia

    Bahadorreza Ofoghi

Authors
  1. Bahadorreza Ofoghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Yearwood
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer and Information Science, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Jiuyong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ofoghi, B., Yearwood, J. (2010). Learning Parse-Free Event-Based Features for Textual Entailment Recognition. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17432-2_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17431-5

  • Online ISBN: 978-3-642-17432-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation