Abductive Reasoning with a Large Knowledge Base for Discourse Processing

  • Ekaterina OvchinnikovaEmail author
  • 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)


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.


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.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Ekaterina Ovchinnikova
    • 1
    Email author
  • 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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