基于相关性约束的隐喻理解方法

Metaphor Comprehension Method Based on Relevance Constraints

隐喻理解已成为语言学、认知学、计算机科学等研究的重要课题,也是自然语言处理中不可避免的任务.提出一种基于相关性约束的隐喻理解方法,利用隐含的相关角度计算目标域和源域的相关程度.首先,基于词、词的主题及语篇的主题扩展出多层次的语义表示;然后,利用上下文信息的相关关系,构建多层次的相关性模型,模型通过多种角度的相关关系将跨层次的语义信息关联起来;接着,采用random walk的方法,通过迭代计算获得隐含角度的相关关系;最后,选择与目标域具有最大相关度的属性作为隐喻理解的结果.将模型应用到隐喻理解任务中,实验结果表明,该方法能够有效地实现隐喻自动理解.

Metaphor comprehension has become an important issue of linguistics, cognitive science and computer science. It is also an unavoidable task of natural language processing. This paper presents a novel metaphor comprehension method to make full use of global information based on relevance constraints. The method uses implied perspective to calculate the relevance degree between the target and source domains. First, multi-level semantic representation is obtained based on the semantic representation of word, topic features of word and topic features of discourse. Next, the degree of relevance relations is calculated and the relevance model is generated. Additionally, relevance relations is used to connect cross-level nodes from different perspectives. Then, using random walk algorithm, the relevance relations are acquired from latent perspectives through iterative computations. Finally, the target attribute that has the maximum relevance degree with the target domain is selected as the comprehension result. Experimental results show that the presented method is effective in metaphor comprehension.