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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Deep vs. Shallow Semantic Analysis Applied to Textual Entailment Recognition
verfasst von : Óscar Ferrández, Rafael Muñoz Terol, Rafael Muñoz, Patricio Martínez-Barco, Manuel Palomar
Erschienen in: Advances in Natural Language Processing
Verlag: Springer Berlin Heidelberg
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This paper covers two different methods of recognising entailment between the text/hypothesis pair by processing logic forms. These two methods are based on knowledge sources. The logic forms of both the text and the hypothesis are inferred by analysing the syntactic dependency relationships between their words. Both approaches use the WordNet lexical database as knowledge source and obtain a semantic similarity score by means of WordNet relations. The difference between them is the treatment of these relations. Whereas one method carries out a deeper analysis considering many WordNet relations, the other one is shallower and manages only a reduced number of relations. These two approaches have been evaluated using the PASCAL Second RTE Challenge data and evaluation methodology.