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.国家自然科学基

基于动态分类的隐喻识别方法

隐喻计算是自然语言处理领域中的重要问题.本文尝试以差异性计算为基础,结合语言、心理和认知的角度对英语隐喻识别进行了深入的分析和探索.对人类而言,隐喻识别是一个动态分类的过程,动态分类是从多个角度来度量事物之间的差异性.本文研究了如何模仿人类来获取概念的特征、选择分类角度、在特定分类角度下计算差异性,并进行了英语名词性隐喻识别的实验.本文的方法对隐喻/常规表达识别的准确率达到85.4%,实验结果表明本文的方法是有效的

基于动态分类的隐喻识别方法

隐喻计算是自然语言处理领域中的重要问题.尝试以差异性计算为基础,结合语言、心理和认知的角度对英语隐喻识别进行深入分析和探索对人类而言,隐喻识别是一个动态分类的过程,动态分类是从多个角度来度量事物之间的差异性.研究了如何模仿人类来获取概念的特征、选择分类角度、在特定分类角度下计算差异性,并进行了英语名词性隐喻识别的实验.该方法对隐喻/常规表达识别的准确率达到85.4%,实验结果表明,该方法是有效的.国家自然科学基金(61075058)~