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.