Abstract
This paper presents a maximum entropy (ME)-based model for Chinese noun phrase metaphor recognition. The metaphor recognizing process will be viewed as a classification task between metaphor and literal meaning. Our experiments show that the metaphor recognizer based on the ME method is significantly better than the Example-based methods within the same context windows. In addition, performance is further improved by introducing additional features into the ME model and achieves good results in window (-2,+2).
Supported by the National Grand Fundamental Research 973 Program of China under Grant No. 2004CB318102; the National High-Tech Research and Development Plan of China under Grant Nos. 2001AA114210, 2002AA117010 (863); the National Natural Science Foundation of China Under Grant No.60473138.
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Wang, Z., Wang, H., Duan, H., Han, S., Yu, S. (2006). Chinese Noun Phrase Metaphor Recognition with Maximum Entropy Approach. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2006. Lecture Notes in Computer Science, vol 3878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671299_25
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DOI: https://doi.org/10.1007/11671299_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32205-4
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