The PASCAL Recognising Textual Entailment Challenge

Ido Dagan; Oren Glickman; Bernardo Magnini

doi:10.1007/11736790_9

The PASCAL Recognising Textual Entailment Challenge

Ido Dagan, Oren Glickman, Bernardo Magnini

ITC-IRST

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1109 Scopus citations

Abstract

This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark¹. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.

Original language	English
Title of host publication	Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.
Pages	177-190
Number of pages	14
DOIs	https://doi.org/10.1007/11736790_9
State	Published - 2006
Event	1st PASCAL Machine Learning Challenges Workshop, MLCW 2005 - Southampton, United Kingdom Duration: 11 Apr 2005 → 13 Apr 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3944 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st PASCAL Machine Learning Challenges Workshop, MLCW 2005
Country/Territory	United Kingdom
City	Southampton
Period	11/04/05 → 13/04/05

Access to Document

10.1007/11736790_9

Cite this

Dagan, I., Glickman, O., & Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge. In Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap. (pp. 177-190). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3944 LNAI). https://doi.org/10.1007/11736790_9

Dagan, Ido ; Glickman, Oren ; Magnini, Bernardo. / The PASCAL Recognising Textual Entailment Challenge. Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.. 2006. pp. 177-190 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark1. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.",

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Dagan, I , Glickman, O & Magnini, B 2006, The PASCAL Recognising Textual Entailment Challenge. in Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3944 LNAI, pp. 177-190, 1st PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, United Kingdom, 11/04/05. https://doi.org/10.1007/11736790_9

The PASCAL Recognising Textual Entailment Challenge. / Dagan, Ido ; Glickman, Oren; Magnini, Bernardo.
Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.. 2006. p. 177-190 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3944 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Dagan I , Glickman O, Magnini B. The PASCAL Recognising Textual Entailment Challenge. In Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.. 2006. p. 177-190. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11736790_9

The PASCAL Recognising Textual Entailment Challenge

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