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| 面向隐喻识别的词语抽象性度量 | |
| 引用本文： | 贾玉祥,昝红英,范 明,俞士汶,王治敏.面向隐喻识别的词语抽象性度量[J].中文信息学报,2017,31(3):41-47. |
| 作者姓名： | 贾玉祥 昝红英 范 明 俞士汶 王治敏 |
| 作者单位： | 1. 郑州大学 信息工程学院,河南 郑州 450001; 2. 北京大学 计算语言学教育部重点实验室,北京 100871; 3. 北京语言大学 汉语学院,北京 100083 |
| 基金项目： | 国家自然科学基金(61402419, 61170163);国家社会科学基金(14BYY096);国家重点基础研究发展计划 973 课题(2014CB340504);计算语言学教育部重点实验室(北京大学)开放课题(201301, 201401) |
| 摘 要： | 隐喻通常借助具体的概念来表达抽象的概念。如果能判断出文本中词语所指的概念是具体还是抽象的,即度量出词语的抽象程度,那么这将为隐喻的机器识别提供重要的依据。该文提出基于跨语言知识迁移的汉语词语抽象性度量方法,把英语中的词语抽象性知识迁移到汉语中来。提出基于词语抽象性知识的隐喻识别方法,并详细分析了词语抽象性与隐喻之间的关系。实验表明,知识迁移是可行的,基于抽象性知识的隐喻识别有较高的准确率,可以有效提高从真实文本中抽取隐喻的效率。 |
| 关 键 词： | 隐喻识别 词语抽象性 跨语言知识迁移 |
Measuring Word Abstractness for Metaphor Recognition |
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| JIA Yuxiang,ZAN Hongying,FAN Ming,YU Shiwen,WANG Zhimin.Measuring Word Abstractness for Metaphor Recognition[J].Journal of Chinese Information Processing,2017,31(3):41-47. | |
| Authors: | JIA Yuxiang ZAN Hongying FAN Ming YU Shiwen WANG Zhimin |
| Affiliation: | 1. School of Information Engineering, Zhengzhou University, Zhengzhou, Henan 450001, China; 2. MOE Key Laboratory of Computational Linguistics, Peking Univerisity, Beijing 100871, China; 3. College of Chinese Studies, Beijing Language and Culture University, Beijing 100083, China |
| Abstract: | In metaphors, abstract things are usually described in terms of concrete things. If we can decide whether a word is concrete or abstract, we will provide useful clues for automatic metaphor recognition. This paper proposed a cross-lingual knowledge transfer method to adapt English word abstractness knowledge to Chinese. Then we propose a metaphor recognition method based on word abstractness and analyze in detail the relation between word abstractness and metaphor. Experimental results show that, the cross-lingual knowledge transfer method is feasible to measure Chinese word abstractness, the abstractness-based metaphor recognition method achieves a high precision score, and it can improve the efficiency of metaphor extraction from real texts. |
| Keywords: | metaphor recognition word abstractness cross-lingual knowledge transfer |
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