Abductive Reasoning with a Large Knowledge Base for Discourse Processing View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

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

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. More... »

PAGES

107-127

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-007-7284-7_7

DOI

http://dx.doi.org/10.1007/978-94-007-7284-7_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004542709


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