Abstract
Due to the increasing amount of opinion data on the internet, opinion mining has become a hot topic, in which extracting opinion targets is a key step. The state-of-the-art approaches only use direct dependency relation patterns to extract opinion targets and the indirect dependency relation patterns have not been used. In this paper, the dependency relations between opinion target and opinion word are defined, and direct and indirect dependency relation patterns are designed. Then, a bootstrapping approach is used to extract and evaluate both candidate patterns and opinion targets. The experimental results show that in formal text, the approach improves the performance compared with the state-of-the-art approaches for opinion target extraction.
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Acknowledgements
Thanks to the Research Center for Social Computing and Information Retrieval of Harbin Institute of Technology for providing the Language Technology Platform (LTP). This work was funded by the Fujian Education Department (No. JAT160387) and also funded by the National Natural Science Foundation of China (No. 61300156) and also funded by the Research Program Foundation of Minjiang University (No. MYK17021).
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Yang, XY., Xu, G., Zhang, FQ., Liao, XW., Xu, L. (2018). Opinion Target Extraction for the Chinese Formal Text Based on Dependency Relations. In: Pan, JS., Wu, TY., Zhao, Y., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2017. Smart Innovation, Systems and Technologies, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-319-70730-3_38
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DOI: https://doi.org/10.1007/978-3-319-70730-3_38
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