Textual Entailment recognition is a very difficult task as it is one of the fundamental problems in any semantic theory of natural language. As in many other NLP tasks, Machine Learning may offer important tools to better understand the problem. In this paper, we will investigate the usefulness of Machine Learning algorithms to address an apparently simple and well denned classification problem: the recognition of Textual Entailment. Due to its specificity, we propose an original feature space, the distance feature space, where we model the distance between the elements of the candidate entailment pairs. The method has been tested on the data of the Recognizing Textual Entailment (RTE) Challenge. © Springer-Verlag Berlin Heidelberg 2006.

Pazienza, M.t., Pennacchiotti, M., Zanzotto, F.m. (2006). Learning textual entailment on a distance feature space. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.240-260). Springer Verlag [10.1007/11736790_14].

Learning textual entailment on a distance feature space

PAZIENZA, MARIA TERESA;ZANZOTTO, FABIO MASSIMO
2006-01-01

Abstract

Textual Entailment recognition is a very difficult task as it is one of the fundamental problems in any semantic theory of natural language. As in many other NLP tasks, Machine Learning may offer important tools to better understand the problem. In this paper, we will investigate the usefulness of Machine Learning algorithms to address an apparently simple and well denned classification problem: the recognition of Textual Entailment. Due to its specificity, we propose an original feature space, the distance feature space, where we model the distance between the elements of the candidate entailment pairs. The method has been tested on the data of the Recognizing Textual Entailment (RTE) Challenge. © Springer-Verlag Berlin Heidelberg 2006.
1st PASCAL Machine Learning Challenges Workshop, MLCW 2005
Southampton, gbr
2005
PASCAL
Rilevanza internazionale
contributo
2006
Settore INF/01 - INFORMATICA
Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
English
Computer Science (all); Theoretical Computer Science
10.1007/11736790_14
Intervento a convegno
Pazienza, M.t., Pennacchiotti, M., Zanzotto, F.m. (2006). Learning textual entailment on a distance feature space. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.240-260). Springer Verlag [10.1007/11736790_14].
Pazienza, Mt; Pennacchiotti, M; Zanzotto, Fm
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/165457
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