Key points are not available for this paper at this time.
In this paper, we propose an unsupervised method for discovering inference rules from text, such as is author of Y a X wrote Y, solved Y a X found a solution to Y, and caused Y a 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.
Lin et al. (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: