Paper: Learning First-Order Horn Clauses from Web Text

ACL ID D10-1106
Title Learning First-Order Horn Clauses from Web Text
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Even the entire Web corpus does not explic- itly answer all questions, yet inference can un- cover many implicit answers. But where do inference rules come from? This paper investigates the problem of learn- ing inference rules from Web text in an un- supervised, domain-independent manner. The SHERLOCK system, described herein, is a first-order learner that acquires over 30,000 Horn clauses from Web text. SHERLOCK em- bodies several innovations, including a novel rule scoring function based on Statistical Rel- evance (Salmon et al., 1971) which is effec- tive on ambiguous, noisy and incomplete Web extractions. Our experiments show that in- ference over the learned rules discovers three times as many facts (at precision 0.8) as the TEXTRUNNER system which merely extracts facts explicitly st...