A Probabilistic Classification Approach for Lexical Textual Entailment

62 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Good Question! Statistical Ranking for Question Generation (2010, background): “7 E.g., Dolan and Brockett (2005) and Glickman et al. (2005) report κ values around 0.6 for paraphrase identiﬁcation and textual entailment, respectively. generated from three corpora.”
A Probabilistic Setting and Lexical Coocurrence Model for Textual Entailment (2005, methodology): “Judges were asked to annotate an example as true if given the text they can infer with high confidence that the hypothesis is true (similar to the guidelines published for the RTE Challenge data-set).”
Learning causality for news events prediction (2012, background): “In textual entailment [14], the task is to identify texts that logically follow one another.”
Inferring textual entailment with a probabilistically sound calculus* (2009, background): “The relationship between the work of Glickman et al. (2005) and our work is remotely similar to the diﬀerence between generative and discriminative models for classiﬁcation problems.”