Abstract
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.
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Ferrández, Ó., Terol, R.M., Muñoz, R., Martínez-Barco, P., Palomar, M. (2006). Deep vs. Shallow Semantic Analysis Applied to Textual Entailment Recognition. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds) Advances in Natural Language Processing. FinTAL 2006. Lecture Notes in Computer Science(), vol 4139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816508_24
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DOI: https://doi.org/10.1007/11816508_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37334-6
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