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2007

LEDIR: An Unsupervised Algorithm for Learning Directionality of Inference Rules

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LEDIR: An Unsupervised Algorithm for Learning Directionality of Inference Rules
Semantic inference is a core component of many natural language applications. In response, several researchers have developed algorithms for automatically learning inference rules from textual corpora. However, these rules are often either imprecise or underspecified in directionality. In this paper we propose an algorithm called LEDIR that filters incorrect inference rules and identifies the directionality of correct ones. Based on an extension to Harris’s distributional hypothesis, we use selectional preferences to gather evidence of inference directionality and plausibility. Experiments show empirical evidence that our approach can classify inference rules significantly better than several baselines.
Rahul Bhagat, Patrick Pantel, Eduard H. Hovy
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Rahul Bhagat, Patrick Pantel, Eduard H. Hovy
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