Abstract
Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims to extract the aspect terms, corresponding opinion terms and sentiment polarity in a target sentence. Most previous methods treat AFOE as word-level or span-level task, which ignore the complementarity of these two tasks. To integrate the merits of word-level and span-level information, we construct an end-to-end Span-based Multi-Table Labeling (SpanMTL) framework. SpanMTL combines word-based and span-based table labeling to tackle AFOE task. Specifically, in the proposed model, we use two separate BiLSTMs to encode the information of aspect and opinion terms into a word-based 2D representation table. Based on the table, we construct span-based table with CNN by associating the word-pair representations. At last, we integrate the table label distributions of word- and span-based table labeling to generate a multi-table labeling. The proposed method improves the performances of Opinion Pair Extraction (OPE) and Opinion Triplet Extraction (OTE) tasks by introducing span information, especially on the datasets with lots of spans. We have conducted various experiments on AFOE datasets to validate our method. The experimental results show that our method outperforms other baselines when the sentences having lots of span information.
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All data generated or analyzed during this study are included in this published article: Wu, Z., Ying, C., Zhao, F., Fan, Z., Dai, X., Xia, R. used grid tagging scheme for aspect-oriented fine-grained opinion extraction. arXiv preprint arXiv:2010.04640 (2020).
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Funding
This work was partially supported by grants from the National Natural Science Foundation of China (No.U1933114, No.62172418), Open Project Foundation of Information Security Evaluation Center of Civil Aviation, China (No.ISECCA-202005), and Fundamental Research Funds for the Central Universities of Civil Aviation University of China (No.3122022091).
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Xing, Y., Zhu, Y., Fan, W. et al. SpanMTL: a span-based multi-table labeling for aspect-oriented fine-grained opinion extraction. Soft Comput 27, 4627–4637 (2023). https://doi.org/10.1007/s00500-022-07721-5
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DOI: https://doi.org/10.1007/s00500-022-07721-5