Textual Entailment by Generality

被引：2

作者：

Pais, Sebastiao ^{[1
]}

Dias, Gael ^{[1
,2
]}

Wegrzyn-Wolska, Katarzyna ^{[3
]}

Mahl, Robert ^{[4
]}

Jouvelot, Pierre ^{[4
]}

机构：

[1] HULTIG Univ Beira Interior, Covilha, Portugal

[2] Univ Caen Basse Normandie, DLU, GREYC, F-14032 Caen, France

[3] Ecole Super dIngenieurs Informat, Genie des Telecommun, SITR, Villejuif, France

[4] Ecole Natl Superieure des Mines de Paris, CRI, Paris, France

来源：

COMPUTATIONAL LINGUISTICS AND RELATED FIELDS | 2011年 / 27卷

关键词：

Asymmetric Association Measures; Informative Asymmetric Measure; Textual Entailment;

D O I：

10.1016/j.sbspro.2011.10.606

中图分类号：

H0 [语言学];

学科分类号：

030303 ; 0501 ; 050102 ;

摘要：

Textual Entailment consists in determining if an entailment relation exists between two texts. In this paper, we present an Informative Asymmetric Measure called the Asymmetric InfoSimba (AIS), which we combine with different asym-metric association measures to recognize the specific case of Textual Entailment by Generality. In particular, the AIS proposes an unsupervised, language-independent, threshold free solution. This new measure is tested against the first Recognizing Textual Entailment dataset for an exhaustive number of asymmetric association measures and shows that the combination of the AIS with the Braun-Blanket steadily improves results against competitive measures such as the one proposed by [1]. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of PACLING Organizing Committee.

引用

页码：258 / 266

页数：9

共 50 条

[41] Building a Greek corpus for Textual Entailment
Marzelou, Evi
Zourari, Maria
Giouli, Voula
Piperidis, Stelios
[J]. SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008, 2008, : 1680 - 1686
[42] Recognizing textual entailment via atomic propositions
Akhmatova, Elena
Molla, Diego
[J]. MACHINE LEARNING CHALLENGES: EVALUATING PREDICTIVE UNCERTAINTY VISUAL OBJECT CLASSIFICATION AND RECOGNIZING TEXTUAL ENTAILMENT, 2006, 3944 : 385 - 403
[43] Learning textual entailment on a distance feature space
Pazienza, Maria Teresa
Pennacchiotti, Marco
Zanzotto, Fabio Massimo
[J]. MACHINE LEARNING CHALLENGES: EVALUATING PREDICTIVE UNCERTAINTY VISUAL OBJECT CLASSIFICATION AND RECOGNIZING TEXTUAL ENTAILMENT, 2006, 3944 : 240 - 260
[44] Compositional Evaluation on Japanese Textual Entailment and Similarity
Yanaka, Hitomi
Mineshima, Koji
[J]. arXiv, 2022,
[45] Techniques for recognizing textual entailment and semantic equivalence
Herrera, Jesus
Penas, Anselmo
Verdejo, Felisa
[J]. CURRENT TOPICS IN ARTIFICIAL INTELLIGENCE, 2006, 4177 : 419 - 428
[46] Exploring Prompting Approaches in Legal Textual Entailment
Onur Bilgin
Logan Fields
Antonio Laverghetta
Zaid Marji
Animesh Nighojkar
Stephen Steinle
John Licato
[J]. The Review of Socionetwork Strategies, 2024, 18 : 75 - 100
[47] Design and realization of a modular architecture for textual entailment
Pado, Sebastian
Noh, Tae-Gil
Stern, Asher
Wang, Rui
Zanoli, Roberto
[J]. NATURAL LANGUAGE ENGINEERING, 2015, 21 (02) : 167 - 200
[48] Paraphrase Identification Using Textual Entailment Recognition
Seethamol, S.
Manju, K.
[J]. 2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, INSTRUMENTATION AND CONTROL TECHNOLOGIES (ICICICT), 2017, : 1071 - 1074
[49] Exploring Prompting Approaches in Legal Textual Entailment
Bilgin, Onur
Fields, Logan
Laverghetta Jr, Antonio
Marji, Zaid
Nighojkar, Animesh
Steinle, Stephen
Licato, John
[J]. REVIEW OF SOCIONETWORK STRATEGIES, 2024, 18 (01): : 75 - 100
[50] Word Segmentation Refinement by Wikipedia for Textual Entailment
Lin, Chuan-Jie
Tu, Yu-Cheng
[J]. 2014 IEEE 15TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI), 2014, : 600 - 606

← 1 2 3 4 5 →