DIRT @SBT@discovery of inference rules from text

Authors:
Dekang Lin;Patrick Pantel
Affiliations:
University of Alberta, Edmonton, Alberta T6H 2E1 Canada;University of Alberta, Edmonton, Alberta T6H 2E1 Canada
Venue:
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2001

Citing 16
Cited 134

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Abstract

In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y ≈ X wrote Y", "X solved Y ≈ X found a solution to Y", and "X caused Y ≈ Y is triggered by X". Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.