A Lexical Alignment Model for Probabilistic
Ido Dagan, Oren Glickman and Moshe Koppel
Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop
Lecture Notes in Computer Science
This paper describes the Bar-Ilan system participating in
the Recognising Textual Entailment Challenge. The paper proposes first
a general probabilistic setting that formalizes the notion of textual entailment.
We then describe a concrete alignment-based model for lexical
entailment, which utilizes web co-occurrence statistics in a bag of words
representation. Finally, we report the results of the model on the Recognising
Textual Entailment challenge dataset along with some analysis.