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Abductive Reasoning with a Large Knowledge Base for Discourse Processing

2013; Springer Nature (Netherlands); Linguagem: Inglês

10.1007/978-94-007-7284-7_7

2542-9388

Ekaterina Ovchinnikova, Niloofar Montazeri, Theodore Alexandrov, Jerry R. Hobbs, Michael C. McCord, Rutu Mulkar-Mehta,

Semantic Web and Ontologies

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.