Abstract
Lexical inference problem is a significant component of some recent core AI and NLP research problems like machine reading and textual entailment. In this paper, we propose method utilizing the Probabilistic Soft Logic (PSL) model for Chinese lexical inference. The proposed PSL model not only can integrate two complementary traditional methods, i.e., the lexical-knowledge-based method and the distributional probabilistic method, but also can optimize the lexical inference network in a global view by the transitivity property of entailment relations. We build a large domain specific verb inference corpus containing 18,029 verb pairs with gold inference labels from math world problems. A five-folded experiment is performed. Results show that the proposed PSL model greatly outperforms our baseline.
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