Generating Triples Based on Dependency Parsing for Contradiction Detection

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 568)


Contradiction detection is a task to detect the contradictory relation between two texts. In the social media, the phenomenon of contradictory descriptions of the same event is common and harmful. It is urgent to detect contradictory texts. Previous methods on detecting contradiction are mostly deriving features from shallow semantic representations like predicate-argument structures. They meet a problem of the low coverage of contradiction. We propose a joint method to extract more contradiction pairs. We utilize dependency parsing tree to generate tripes (dp-triple) which represent semantic information of the text. The dp-triple extraction method extract more contradiction pairs than present shallow semantic extraction methods like open IE or SRL. Due to the coverage limitation of triples, we also derive features from the context of the matching words between texts as backup. We demonstrate the joint method is effective in detecting contradiction. In predicting stage, we use a unsupervised method to detect contradiction relation and achieve a better performance than the state of the art method.


Contradiction Detection Dependency Parsing Shallow Semantic Representation Word Matching Joint Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bentivogli, L., Dagan, I., Dang, H.T., Giampiccolo, D., Magnini, B.: The fifth pascal recognizing textual entailment challenge. Proc. TAC 9, 14–24 (2009)Google Scholar
  2. 2.
    Clark, P., Harrison, P.: Recognizing textual entailment with logical inference. In: Text Analysis Conference (TAC 2008) Workshop-RTE-4 Track, National Institute of Standards and Technology (NIST). Citeseer (2008)Google Scholar
  3. 3.
    Condoravdi, C., Crouch, D., De Paiva, V., Stolle, R., Bobrow, D.G.: Entailment, intensionality and text understanding. In: Proceedings of the HLT-NAACL 2003 Workshop on Text Meaning, vol. 9, pp. 38–45. Association for Computational Linguistics (2003)Google Scholar
  4. 4.
    Dagan, I., Dolan, B., Magnini, B., Roth, D.: Recognizing textual entailment: Rational, evaluation and approaches-erratum. Nat. Lang. Eng. 16(01), 105–105 (2010)CrossRefGoogle Scholar
  5. 5.
    Dagan, I., Glickman, O., Magnini, B.: The PASCAL recognising textual entailment challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 177–190. Springer, Heidelberg (2006) Google Scholar
  6. 6.
    De Marneffe, M.C., Rafferty, A.N., Manning, C.D.: Finding contradictions in text. ACL 8, 1039–1047 (2008)Google Scholar
  7. 7.
    Giampiccolo, D., Dang, H.T., Magnini, B., Dagan, I., Cabrio, E., Dolan, B.: The fourth pascal recognizing textual entailment challenge. In: Proceedings of the First Text Analysis Conference (TAC 2008). Citeseer (2009)Google Scholar
  8. 8.
    Giampiccolo, D., Magnini, B., Dagan, I., Dolan, B.: The third pascal recognizing textual entailment challenge. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 1–9. Association for Computational Linguistics (2007)Google Scholar
  9. 9.
    Harabagiu, S., Hickl, A., Lacatusu, F.: Negation, contrast and contradiction in text processing. AAAI 6, 755–762 (2006)Google Scholar
  10. 10.
    Hashimoto, C., Torisawa, K., De Saeger, S., Oh, J.H., Kazama, J.: Excitatory or inhibitory: a new semantic orientation extracts contradiction and causality from the web. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 619–630. Association for Computational Linguistics (2012)Google Scholar
  11. 11.
    Marelli, M., Bentivogli, L., Baroni, M., Bernardi, R., Menini, S., Zamparelli, R.: Semeval-2014 task 1: evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. SemEval-2014 (2014)Google Scholar
  12. 12.
    Pham, M.Q.N., Nguyen, M.L., Shimazu, A.: Using shallow semantic parsing and relation extraction for finding contradiction in text (2013)Google Scholar
  13. 13.
    Ritter, A., Downey, D., Soderland, S., Etzioni, O.: It’s a contradiction–no, it’s not: a case study using functional relations. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 11–20. Association for Computational Linguistics (2008)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2015

Authors and Affiliations

  1. 1.Research Center for Social Computing and Information RetrievalHarbin Institute of TechnologyHarbinChina

Personalised recommendations