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 (Proceedings of IWCS 2011)
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.