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New Technology of Library and Information Service  2015, Vol. 31 Issue (4): 34-40    DOI: 10.11925/infotech.1003-3513.2015.04.05
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An Approach to Chinese Metaphor Identification Based on Word Abstractness
Huang Xiaoxi1,2, Zhang Hua1, Lu Bei1, Wang Rongbo1, Wu Ting1
1 Institution of Cognitive and Intelligent Computing, Hangzhou Dianzi University, Hangzhou 310018, China;
2 Center for the Study of Language and Cognition, Zhejiang University, Hangzhou 310028, China
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Abstract  

[Objective] Design a method to automatically compute Chinese word abstractness, and introduce it into metaphor identification task in natural language understanding. [Methods] The word abstractness is computed by logistic regression model. The features are the word vectors computed by neural network model and the feature weight vectors come from a hand coded abstractness dictionary. A metaphor identification algorithm based on word abstractness is proposed to demonstrate the validity of this method. [Results] By comparing with the existing methods of word abstractness computing, this method has better accordance with human cognition and is an effective method in metaphor identification task. [Limitations] The utilization of word vectors for word abstractness is defective. The scale of the abstract words affects the learning of feature weight vectors. [Conclusions] Word abstractness computing reflects the ability to concept classification, Chinese word abstractness computed by this method is better fitting the human cognition, and the experimental results show that word abstractness can improve the effect of metaphor identification.

Key wordsWord abstractness      Neural network language model      Metaphor identification     
Received: 28 October 2014      Published: 21 May 2015
:  TP391  

Cite this article:

Huang Xiaoxi, Zhang Hua, Lu Bei, Wang Rongbo, Wu Ting. An Approach to Chinese Metaphor Identification Based on Word Abstractness. New Technology of Library and Information Service, 2015, 31(4): 34-40.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.04.05     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I4/34

[1] Graesser A C, McNamara D S, Louwerse M M, et al. Coh-Metrix: Analysis of Text on Cohesion and Language [J]. Behavior Research Methods, Instruments, & Computers, 2004, 36(2): 193-202.
[2] McCarthy P M, Renner A M, Duncan M G, et al. Identifying Topic Sentencehood [J]. Behavior Research Methods, 2008, 40(3): 647-664.
[3] Feng S, Cai Z, Crossley S A, et al. Simulating Human Ratings on Word Concreteness [C]. In: Proceedings of the 24th International Florida Artifical Intelligence Research Society Conference, Palm Beach, Florida, USA. 2011: 245-250.
[4] Gargett A, Ruppenhofer J, Barnden J. Dimensions of Metaphorical Meaning[C]. In: Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon, 2014: 166-173.
[5] Turney P, Neuman Y, Assaf D, et al. Literal and Metaphorical Sense Identification Through Concrete and Abstract Context [C]. In: Proceedings of the 2011 Conference on the Empirical Methods in Natural Language Processing, Edinburgh, UK. 2011:680-690.
[6] 龙涛. 抽象名词的隐喻性“有界”空间范畴义[J]. 武汉大学学报: 人文科学版, 2011, 64(4): 112-117. (Long Tao. Metaphorical “Bounded” Space Category Meaning of Abstract Nouns [J]. Wuhan University Journal: Humanity Sciences, 2011, 64(4): 112-117.)
[7] 官杨. 程度副词修饰名词浅析[J]. 安徽文学(下半月), 2008(12): 307-308. (Guan Yang. Analysis of the Nouns Modified by Adverbs [J]. Anhui Literature, 2008(12): 307-308.)
[8] 鲁晓雁. 抽象名词语义搭配情况调查(之一)[J]. 学术交流, 2002(2): 109-112. (Lu Xiaoyan. An Investigation into the Semantic Collocation of Abstract Nouns [J]. Academic Exchange, 2002(2): 109-112.)
[9] 赵红艳. 基于语义知识的动词隐喻识别与应用[D]. 南京: 南京师范大学, 2012. (Zhao Hongyan. Chinese Verb Metaphor Recognition and Application Based on Semantic Knowledge [D]. Nanjing: Nanjing Normal University, 2012.)
[10] 杨玉玲. 认知凸显性和带“有”的相关格式[J]. 修辞学习, 2007(5): 31-34. (Yang Yuling. Cognitive Salience and the Contained “Have” Sentence Format [J]. Rhetoric Learning, 2007(5): 31-34.)
[11] Bengio Y, Ducharme R, Vincent P, et al. A Neural Probabilistic Language Mode [J]. Journal of Machine Learning Research, 2003, 3: 1137-1155.
[12] Coltheart M. The MRC Psycholinguistic Database [J]. The Quarterly Journal of Experimental Psychology, 1981, 33(4): 497-505.
[13] Lakoff G, Johnsen M. Metaphors We Live by [M]. The University of Chicago Press, 1980.
[14] Lafferty J, McCallum A, Pereira F C N. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data [C]. In: Proceedings of the 18th International Conference on Machine Learning. 2001: 282-289.
[15] Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods [M]. Cambridge University Press, 2000.
[16] 刘挺, 车万翔, 李正华. 语言技术平台[J]. 中文信息学报, 2011, 25(6): 53-62. (Liu Ting, Che Wanxiang, Li Zhenghua. Language Technology Platform [J]. Journal of Chinese Information Processing, 2011, 25(6): 53-62.)

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