Abstract
The Chinese language is a character-based language, with no explicit separators between words like English. Traditionally, word segmentation is conducted to convert Chinese sentences into word sequences, thus the same framework of English sentiment analysis can be exploited for Chinese. These work uses a specified word segmentor as a prerequisite step, yet ignores the fact that different segmentation styles exist in Chinese word segmentation, such as CTB, PKU, MSR and etc. In this paper, we study the influences of these heterogeneous segmentations for Chinese sentiment analysis, and then integrate these segmentations, based on both discrete and neural models. Experimental results show that different segmentations do affect the final performances, and the integrated models can achieve better performances.
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Acknowledgments
We thank the anonymous reviewers for their constructive comments, which helped to improve the paper. This study was supported by Natural Science Foundation of Heilongjiang Province under Grant No. F2016036, National Natural Science Foundation of China under Grant No. 61170148, and the Returned Scholar Foundation of Heilongjiang Province, respectively.
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Pan, D., Zhang, M., Fu, G. (2016). Chinese Sentiment Analysis Exploiting Heterogeneous Segmentations. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_32
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DOI: https://doi.org/10.1007/978-3-319-47674-2_32
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