PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient smantic deduction and approximate matching over compact parse forests
Roy Bar Haim, Jonathan Berant, Ido Dagan, Iddo Greental, Shachar Mirkin, Eyal Shnarch and Idan Szpektor
In: TAC 2008, 17-19 Nov 2009, Gaithersburg, Maryland USA.


Semantic inference is often modeled as application of entailment rules, which specify generation of entailed sentences from a source sentence. Efficient generation and representation of entailed consequents is a fundamental problem common to such inference methods. We present a new data structure, termed "compact forest", which allows efficient generation and representation of entailed consequents, each represented as a parse tree. Rule-based inference is complemented with a new approximate matching measure inspired by tree kernels, which is computed efficiently over compact forests. Our system also makes use of novel large-scale entailment rule bases, derived from Wikipedia as well as from information about predicates and their argument mapping, gathered from available lexicons and complemented by unsupervised learning.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:4699
Deposited By:Roy Bar Haim
Deposited On:24 March 2009