在此前的汉语未登录词语义预测中,构词相关的知识一直被当做预测的手段,而没有被视为一种有价值的知识表示方式,该文在“语素概念”基础上,深入考察汉语的语义构词知识,给出未登录词的“多层面”的词义知识表示方案。针对该方案,该文采用贝叶斯网络方法,构建面向汉语未登录词的自动语义构词分析模型,该模型能有效预测未登录词的“多层面”的词义知识。这种词义知识表示简单、直观、易于拓展,实验表明对汉语未登录词的语义预测具有重要的价值,可以满足不同层次的应用需求。
Abstract
In the previous researches in sense prediction of Chinese unknown words, the lexical knowledge related to word-formation has been used but not regarded as a valuable form of knowledge representation. This paper, on the basis of the morphemic concepts, provides a multi-level solution to knowledge representation of Chinese unknown words. A model based on Bayesian network is also constructed to analyze semantic word-formation of Chinese unknown words, effectively predicting the multi-level lexical knowledge of Chinese unknown words. This kind of lexical knowledge representation is simple, intuitive and easy to expand. Experimental results show that, this knowledge representation is of important value in sense guessing of Chinese unknown words, and can meet the application needs on different levels.
关键词
未登录词 /
词义知识表示 /
语义预测 /
语义构词
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Key words
Chinese unknown words /
lexical knowledge representation /
sense prediction /
semantic word formation
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脚注
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基金
国家社科基金(16BYY137);国家重点基础研究发展计划资助项目(2014CB340504);国家社科基金(12&ZD119)
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