Abstract
Classification of word semantic relation is a challenging task in natural language processing (NLP) field. In many practical applications, we need to distinguish words with different semantic relations. Much work relies on semantic resources such as Tongyici Cilin and HowNet, which are limited by the quality and size. Recently, methods based on word embedding have received increasing attention for their flexibility and effectiveness in many NLP tasks. Furthermore, word vector offset implies words semantic relation to some extent. This paper proposes a novel framework for identifying the Chinese word semantic relation. We combine semantic dictionary, word vector and linguistic knowledge into a classification system. We conduct experiments on the Chinese Word Semantic Relation Classification shared task of NLPCC 2017. We rank No.1 with the result of F1 value 0.859. The results demonstrate that our method is very scientific and effective.
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