Abstract
Aspect identification, a key sub-task in Aspect-Based Sentiment Analysis (ABSA), aims to identify aspect categories from online user reviews. Inspired by the observation that different segments of a review usually express different aspect categories, we propose a reviews-segmentation-based method to improve aspect identification. Specifically, we divide a review into several segments according to the sentence structure, and then automatically transfer aspect labels from the original review to its derived segments. Trained with the new constructed segment-level dataset, a classifier can achieve better performance for aspect identification. Another contribution of this paper is extracting alignment features, which can be leveraged to further improve aspect identification. The experimental results show the effectiveness of our proposed method.
Supported by the NSFC (No. 61472183, 61672277, 61772261).
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Ning, T., Wu, Z., Dai, XY., Huang, J., Huang, S., Chen, J. (2018). Improving Aspect Identification with Reviews Segmentation. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11108. Springer, Cham. https://doi.org/10.1007/978-3-319-99495-6_35
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