Recognising Textual Entailment Focusing on Non-Entailing Text and Hypothesis

Rongzhou Shen; Thade Nahnsen; Claire Grover; Ewan Klein

Recognising Textual Entailment Focusing on Non-Entailing Text and Hypothesis

Rongzhou Shen, Thade Nahnsen, Claire Grover, Ewan Klein

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

Abstract

This paper describes a predominantly shallow ap-proach to the rte-4 Challenge. We focus our at-tention on the non-entailing Text and Hypothesis pairs in the dataset. The system uses a Maximum Entropy framework to classify each pair of Text and Hypothesis as either yes or no, using a range of different feature sets based on an analysis of the existing non-entailing pairs in rte training data.

Original language	English
Title of host publication	Proceedings of the Fourth PASCAL Challenges Workshop on Recognizing Textual Entailment
Number of pages	8
Publication status	Published - 2008

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Shen Nahnsen et al 2008 Recognising Textual Entailment Focusing on Non Entailing Text and HypothesisAccepted author manuscript, 561 KB

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title = "Recognising Textual Entailment Focusing on Non-Entailing Text and Hypothesis",

abstract = "This paper describes a predominantly shallow ap-proach to the rte-4 Challenge. We focus our at-tention on the non-entailing Text and Hypothesis pairs in the dataset. The system uses a Maximum Entropy framework to classify each pair of Text and Hypothesis as either yes or no, using a range of different feature sets based on an analysis of the existing non-entailing pairs in rte training data.",

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AU - Shen, Rongzhou

AU - Nahnsen, Thade

AU - Grover, Claire

AU - Klein, Ewan

PY - 2008

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N2 - This paper describes a predominantly shallow ap-proach to the rte-4 Challenge. We focus our at-tention on the non-entailing Text and Hypothesis pairs in the dataset. The system uses a Maximum Entropy framework to classify each pair of Text and Hypothesis as either yes or no, using a range of different feature sets based on an analysis of the existing non-entailing pairs in rte training data.

AB - This paper describes a predominantly shallow ap-proach to the rte-4 Challenge. We focus our at-tention on the non-entailing Text and Hypothesis pairs in the dataset. The system uses a Maximum Entropy framework to classify each pair of Text and Hypothesis as either yes or no, using a range of different feature sets based on an analysis of the existing non-entailing pairs in rte training data.

M3 - Conference contribution

BT - Proceedings of the Fourth PASCAL Challenges Workshop on Recognizing Textual Entailment

ER -

Recognising Textual Entailment Focusing on Non-Entailing Text and Hypothesis

Abstract

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