JOURNAL ARTICLE

A probabilistic setting and lexical cooccurrence model for textual entailment

Abstract

This paper proposes a general probabilistic setting that formalizes a probabilistic notion of textual entailment. We further describe a particular preliminary model for lexical-level entailment, based on document cooccurrence probabilities, which follows the general setting. The model was evaluated on two application independent datasets, suggesting the relevance of such probabilistic approaches for entailment modeling.

Keywords:
Logical consequence Textual entailment Probabilistic logic Computer science Natural language processing Relevance (law) Artificial intelligence Statistical model

Metrics

22
Cited By
3.83
FWCI (Field Weighted Citation Impact)
16
Refs
0.94
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Natural Language Processing Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Text Analysis Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence

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