A Probabilistic Setting and Lexical Coocurrence Model for Textual Entailment

By Oren Glickman, Ido Dagan. 2005

31 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Hypothesis Only Baselines in Natural Language Inference (2018, background): “For example, Glickman and Dagan (2005) argue that “the notion of textual entailment is relevant only” for hypothesis that are not world facts, e.g. “Paris is the capital of France.””
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation (2019, methodology): “Co-occurrence Model: (Glickman and Dagan, 2005) Originally proposed for the RTE task, this model computes the probability of a term g j in the n-gram being entailed by the table as the maximum of its probabilities of being en-4 BLEU is usually computed at the corpus-level, however here we show its…”
Context-theoretic Semantics for Natural Language: an Overview (2009, background): “Glickman and Dagan (2005) do not use this measure, possibly because the problem of data sparseness makes it useless for long strings.”
A Context-Theoretic Framework for Compositionality in Distributional Semantics (2011, methodology): “The formulation in the previous section suggests an alternative approach to that of Glickman and Dagan (2005) to cope with the data sparseness problem.”
A Discourse Commitment-Based Framework for Recognizing Textual Entailment (2007, background): “⋄ 2 ⋄ U NALIGNED C HUNK : This feature represents the number of chunks in one text that are not aligned with a chunk from the other ⋄ 3 ⋄ L EXICAL E NTAILMENT P ROBABILITY : Deﬁned as in (Glickman and Dagan, 2005).”