Abductive Reasoning with a Large Knowledge Base for Discourse Processing

  • Ekaterina Ovchinnikova
  • Niloofar Montazeri
  • Theodore Alexandrov
  • Jerry R. Hobbs
  • Michael C. McCord
  • Rutu Mulkar-Mehta
Part of the Text, Speech and Language Technology book series (TLTB, volume 47)

Abstract

This chapter presents a discourse processing framework based on weighted abduction. We elaborate on ideas described in Hobbs et al. (1993) and implement the abductive inference procedure in a system called Mini-TACITUS. Particular attention is paid to constructing a large and reliable knowledge base for supporting inferences. For this purpose we exploit such lexical-semantic resources as WordNet and FrameNet. English Slot Grammar is used to parse text and produce logical forms. We test the proposed procedure and the resulting knowledge base on the recognizing textual entailment task using the data sets from the RTE-2 challenge for evaluation. In addition, we provide an evaluation of the semantic role labeling produced by the system taking the Frame-Annotated Corpus for Textual Entailment as a gold standard.

Keywords

Logical Form Word Sense Good Interpretation Discourse Processing Semantic Role Label 
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. Bar-Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., Magnini, B., & Szpektor, I. (2006). The second PASCAL recognising textual entailment challenge. In Proc. of the second PASCAL challenges workshop on recognising textual entailment. Google Scholar
  2. Burchardt, A., & Pennacchiotti, M. (2008). FATE: A FrameNet-annotated corpus for textual entailment. In Proc. of LREC’08, Marrakech, Morocco. Google Scholar
  3. Burchardt, A., Erk, K., & Frank, A. (2005). A WordNet detour to framenet. In Sprachtechnologie, mobile Kommunikation und linguistische Resourcen (Vol. 8). Google Scholar
  4. Burchardt, A., Pennacchiotti, M., Thater, S., & Pinkal, M. (2009). Assessing the impact of frame semantics on textual entailment. Natural Language Engineering, 15(4), 527–550. CrossRefGoogle Scholar
  5. Clark, P., Harrison, P., Thompson, J., Murray, W., Hobbs, J., & Fellbaum, C. (2007). On the role of lexical and world knowledge in RTE3. In Proc. of the ACL-PASCAL workshop on textual entailment and paraphrasing (pp. 54–59). CrossRefGoogle Scholar
  6. Dagan, I., Dolan, B., Magnini, B., & Roth, D. (2010). Recognizing textual entailment: Rational, evaluation and approaches – erratum. Natural Language Engineering, 16(1), 105. CrossRefGoogle Scholar
  7. Das, D., Schneider, N., Chen, D., & Smith, N. A. (2010). SEMAFOR 1.0: A probabilistic frame-semantic parser (Technical Report CMU-LTI-10-001). Carnegie Mellon University, Pittsburgh, Pennsylvania. Google Scholar
  8. Davidson, D. (1967). The logical form of action sentences. In N. Rescher (Ed.), The logic of decision and action (pp. 81–120). Pittsburgh: University of Pittsburgh Press. Google Scholar
  9. Erk, K., & Pado, S. (2006). Shalmaneser – a flexible toolbox for semantic role assignment. In Proc. of LREC’06, Genoa, Italy. Google Scholar
  10. Fellbaum, C. (Ed.) (1998). WordNet: An electronic lexical database (1st ed.) Cambridge: MIT Press. MATHGoogle Scholar
  11. Garoufi, K. (2007). Towards a better understanding of applied textual entailment: Annotation and evaluation of the RTE-2 dataset. Master’s thesis, Saarland University. Google Scholar
  12. Hobbs, J. R. (1985). Ontological promiscuity. In Proc. of the 23rd annual meeting of the association for computational linguistics, Chicago, Illinois (pp. 61–69). Google Scholar
  13. Hobbs, J. R., Stickel, M., Appelt, D., & Martin, P. (1993). Interpretation as abduction. Artificial Intelligence, 63, 69–142. CrossRefGoogle Scholar
  14. Inoue, N., & Inui, K. (2011). ILP-based reasoning for weighted abduction. In Proc. of AAAI workshop on plan, activity and intent recognition. Google Scholar
  15. Inoue, N., Ovchinnikova, E., Inui, K., & Hobbs, J. R. (2012). Coreference resolution with ILP-based weighted abduction. In Proc. of the 24th international conference on computational linguistics (pp. 1291–1308). Google Scholar
  16. McCord, M. C. (1990). Slot grammar: A system for simpler construction of practical natural language grammars. In In natural language and Logic: International scientific symposium, lecture notes in computer science (pp. 118–145). Berlin: Springer. CrossRefGoogle Scholar
  17. McCord, M. C. (2010). Using slot grammar (Technical report). IBM T. J. Watson Research Center. RC 23978 Revised. Google Scholar
  18. McCord, M. C., Murdock, J. W., & Boguraev, B. K. (2012). Deep parsing in Watson. IBM Journal of Research and Development, 56(3/4), 3:1–3:15. Google Scholar
  19. Mulkar, R., Hobbs, J. R., & Hovy, E. (2007). Learning from reading syntactically complex biology texts. In Proc. of the 8th international symposium on logical formalizations of commonsense reasoning, Palo Alto, USA. Google Scholar
  20. Mulkar-Mehta, R. (2007). Mini-TACITUS. http://www.rutumulkar.com/tacitus.html.
  21. Ovchinnikova, E. (2012). Integration of world knowledge for natural language understanding. Amsterdam: Atlantis Press. CrossRefMATHGoogle Scholar
  22. Ovchinnikova, E., Vieu, L., Oltramari, A., Borgo, S., & Alexandrov, T. (2010). Data-driven and ontological analysis of FrameNet for natural language reasoning. In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proc. of LREC’10. Valletta, Malta: European Language Resources Association (ELRA). Google Scholar
  23. Peñas, A., & Ovchinnikova, E. (2012). Unsupervised acquisition of axioms to paraphrase noun compounds and genitives. In LNCS. Proc. of the international conference on intelligent text processing and computational linguistics, New Delhi, India (pp. 388–401). Berlin: Springer. CrossRefGoogle Scholar
  24. Ruppenhofer, J., Ellsworth, M., Petruck, M., Johnson, C., & Scheffczyk, J. (2006). FrameNet II: Extended theory and practice. Berkele: International Computer Science Institute. Google Scholar
  25. Shen, D., & Lapata, M. (2007). Using semantic roles to improve question answering. In Proc. of EMNLP-CoNLL (pp. 12–21). Google Scholar
  26. Stickel, M. E. (1988). A prolog technology theorem prover: Implementation by an extended prolog compiler. Journal of Automated Reasoning, 4(4), 353–380. MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Ekaterina Ovchinnikova
    • 1
  • Niloofar Montazeri
    • 1
  • Theodore Alexandrov
    • 2
  • Jerry R. Hobbs
    • 1
  • Michael C. McCord
    • 4
  • Rutu Mulkar-Mehta
    • 3
  1. 1.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA
  2. 2.University of BremenBremenGermany
  3. 3.San Diego Supercomputer CenterUniversity of California in San DiegoLa JollaUSA
  4. 4.OssiningUSA

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