Deep vs. shallow semantic analysis applied to textual entailment recognition

Authors:
Óscar Ferrández;Rafael Muñoz Terol;Rafael Muñoz;Patricio Martínez-Barco;Manuel Palomar
Affiliations:
Natural Language Processing and Information Systems Group, Department of Software and Computing Systems, University of Alicante, Alicante, Spain;Natural Language Processing and Information Systems Group, Department of Software and Computing Systems, University of Alicante, Alicante, Spain;Natural Language Processing and Information Systems Group, Department of Software and Computing Systems, University of Alicante, Alicante, Spain;Natural Language Processing and Information Systems Group, Department of Software and Computing Systems, University of Alicante, Alicante, Spain;Natural Language Processing and Information Systems Group, Department of Software and Computing Systems, University of Alicante, Alicante, Spain
Venue:
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Year:
2006

Citing 4
Cited 1

Lexical chains for question answering

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Discovering entailment relations using "textual entailment patterns"

EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
A probabilistic setting and lexical cooccurrence model for textual entailment

EMSEE '05 Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
The PASCAL recognising textual entailment challenge

MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment

A knowledge-based textual entailment approach applied to the AVE task

CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval

Quantified Score

Hi-index	0.00

Visualization

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.