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.
REF developed an algorithm called LEDIR for learning the directionality of non-directional inference rules like those produced by DIRT.
LEDIR: An Unsupervised Algorithm for Learning Directionality of Inference Rules
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science