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Natural Logic and Natural Language Inference

  • Bill MacCartney
  • Christopher D. Manning
Part of the Text, Speech and Language Technology book series (TLTB, volume 47)

Abstract

We propose a model of natural language inference which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. We extend past work in natural logic, which has focused on semantic containment and monotonicity, by incorporating both semantic exclusion and implicativity. Our model decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical entailment relation for each edit; propagates these relations upward through a semantic composition tree according to properties of intermediate nodes; and joins the resulting entailment relations across the edit sequence. A computational implementation of the model achieves 70 % accuracy and 89 % precision on the FraCaS test suite. Moreover, including this model as a component in an existing system yields significant performance gains on the Recognizing Textual Entailment challenge.

Keywords

Natural Logic Inference Method Union Relation Semantic Function Entailment Relation 
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.

References

  1. Bos, J., & Markert, K. (2005). Recognising textual entailment with logical inference. In Proceedings of the conference on human language technology and empirical methods in natural language processing (pp. 628–635). Vancouver: Association for Computational Linguistics. CrossRefGoogle Scholar
  2. Böttner, M. (1988). A note on existential import. Studia Logica, 47(1), 35–40. MathSciNetCrossRefzbMATHGoogle Scholar
  3. Chambers, N., Cer, D., Grenager, T., Hall, D., Kiddon, C., MacCartney, B., de Marneffe, M.-C., Ramage, D., Yeh, E., & Manning, C. D. (2007). Learning alignments and leveraging natural logic. In Proceedings of the ACL-07 workshop on textual entailment and paraphrasing (pp. 165–170). Prague: Association for Computational Linguistics. CrossRefGoogle Scholar
  4. Condoravdi, C., Crouch, D., de Paiva, V., Stolle, R., & Bobrow, D. (2003). Entailment, intensionality and text understanding. In Proceedings of the HLT-NAACL 2003 workshop on text meaning (pp. 38–45). Morristown: Association for Computational Linguistics. CrossRefGoogle Scholar
  5. Cooper, R., et al. (1996). Using the framework (Technical Report LRE 62-051 D-16). The FraCaS Consortium. Google Scholar
  6. Dagan, I., Glickman, O., & Magnini, B. (2006). The PASCAL recognising textual entailment challenge. In J. Quiñonero-Candela, C. E. Rasmussen, F. Sinz, O. Bousquet, & B. Schölkopf (Eds.), Machine learning challenges. Evaluating predictive uncertainty, visual object classification, and recognising textual entailment (Vol. 3944, pp. 177–190). Berlin: Springer. CrossRefGoogle Scholar
  7. Fyodorov, Y., Winter, Y., & Francez, N. (2000). A natural logic inference system. In Proceedings of the 2nd international workshop on inference in computational semantics (ICoS-2), Germany: Dagstuhl. Google Scholar
  8. Giampiccolo, D., Magnini, B., Dagan, I., & Dolan, B. (2007). The third PASCAL recognizing textual entailment challenge. In Proceedings of the ACL-07 workshop on textual entailment and paraphrasing (pp. 1–9). Prague: Association for Computational Linguistics CrossRefGoogle Scholar
  9. Glickman, O., Dagan, I., & Koppel, M. (2005). Web based probabilistic textual entailment. In Proceedings of the PASCAL challenges workshop on recognizing textual entailment. http://u.cs.biu.ac.il/~nlp/RTE1/Proceedings/glickman_et_al.pdf. Google Scholar
  10. Hickl, A., Williams, J., Bensley, J., Roberts, K., Rink, B., & Shi, Y. (2006). Recognizing textual entailment with LCC’s GROUNDHOG system. In Proceedings of the second PASCAL challenges workshop on recognizing textual entailment, Venice, Italy, PASCAL (pp. 137–142). Google Scholar
  11. Lakoff, G. (1970). Linguistics and natural logic. Synthese, 22, 151–271. CrossRefzbMATHGoogle Scholar
  12. MacCartney, B. (2009). Natural language inference. Ph.D. thesis, Stanford University. Google Scholar
  13. MacCartney, B., & Manning, C. D. (2008). Modeling semantic containment and exclusion in natural language inference. In Proceedings of the 22nd international conference on computational linguistics (COLING-08) (pp. 521–528). Manchester: Association for Computational Linguistics. Google Scholar
  14. MacCartney, B., Grenager, T., de Marneffe, M.-C., Cer, D., & Manning, C. D. (2006). Learning to recognize features of valid textual entailments. In Proceedings of the human language technology conference of the North American chapter of the Association of Computational Linguistics (pp. 41–48). New York: Association for Computational Linguistics. CrossRefGoogle Scholar
  15. Nairn, R., Condoravdi, C., & Karttunen, L. (2006). Computing relative polarity for textual inference. In Proceedings of the fifth international workshop on inference in computational semantics (ICoS-5) (pp. 67–76). Google Scholar
  16. MacCartney, B., Galley, M., & Manning, C. D. (2008). A phrase-based alignment model for natural language inference. In Proceedings of the conference on empirical methods in natural language processing (pp. 802–811). Honolulu: Association for Computational Linguistics. CrossRefGoogle Scholar
  17. Ritter, A., Downey, D., Soderland, S., & Etzioni, O. (2008). 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). Honolulu: Association for Computational Linguistics. CrossRefGoogle Scholar
  18. Sánchez Valencia, V. (1991). Studies on natural logic and categorial grammar. Ph.D. thesis, Univ. Amsterdam. Google Scholar
  19. Sukkarieh, J. (2001). Quasi-NL knowledge representation for structurally-based inferences. In Proceedings of the 3rd international workshop on inference in computational semantics (ICoS-3), Siena, Italy. Google Scholar
  20. van Benthem, J. (1988). The semantics of variety in categorial grammars. In W. Buszkowski, W. Marciszewski, & J. van Benthem (Eds.), Categorial grammar (pp. 33–55). Amsterdam: Benjamins. Google Scholar
  21. van Benthem, J. (1991). Studies in logic: Vol. 130. Language in action: Categories, lambdas and dynamic logic. Amsterdam: North-Holland. zbMATHGoogle Scholar
  22. van Benthem, J. (2008). A brief history of natural logic (Technical Report PP-2008-05). Institute for Logic, Language & Computation. http://www.illc.uva.nl/Publications/ResearchReports/PP-2008-05.text.pdf.
  23. van der Sandt, R. A. (1992). Presupposition projection as anaphora resolution. Journal of Semantics, 9(4), 333–377. CrossRefGoogle Scholar
  24. van Eijck, J. (2005). Natural logic for natural language. http://homepages.cwi.nl/~jve/papers/05/nlnl/NLNL.pdf.

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Stanford UniversityStanfordUSA

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