Paper: LEDIR: An Unsupervised Algorithm for Learning Directionality of Inference Rules

ACL ID D07-1017
Title LEDIR: An Unsupervised Algorithm for Learning Directionality of Inference Rules
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

Semantic inference is a core component of many natural language applications. In re- sponse, several researchers have developed algorithms for automatically learning infer- ence rules from textual corpora. However, these rules are often either imprecise or un- derspecified in directionality. In this paper we propose an algorithm called LEDIR that filters incorrect inference rules and identi- fies the directionality of correct ones. Based on an extension to Harris’s distribu- tional hypothesis, we use selectional pref- erences to gather evidence of inference di- rectionality and plausibility. Experiments show empirical evidence that our approach can classify inference rules significantly better than several baselines.