Abductive reasoning with a large knowledge base for discourse processing

Authors:
Ekaterina Ovchinnikova;Jerry R. Hobbs;Niloofar Montazeri;Michael C. McCord;Theodore Alexandrov;Rutu Mulkar-Mehta
Affiliations:
University of Osnabrück;USC ISI;USC ISI;IBM Research;University of Bremen;USC ISI
Venue:
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Year:
2011

Citing 6
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Abstract

This paper 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. We test the proposed procedure and the obtained 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.