Abstract
Aspect-opinion pair extraction (AOPE) task, aiming at extracting aspect terms and their corresponding opinion terms in pairs, has caused widespread attention in recent years. Most studies focus on incorporating external knowledge, such as syntactic information. However, they are limited by the inadequate ability to capture long-distance information, and the utilization of external knowledge is more costly. In this paper, we propose AOPSS, a joint learning framework, to explore the AOPE task as semantic segmentation. As in most prior studies, we divide the AOPE task into two subtasks: entity recognition and relation detection. Specifically, AOPSS can synchronously capture task-invariant and task-specific features for the two subtasks without integrating any additional knowledge. Furthermore, we consider the interaction between entity and relation feature representations, which can improve the mutual heuristic effect for the two subtasks. Experimental results illustrate that our method achieves state-of-the-art performance on four public datasets, and we take further analysis to demonstrate the effectiveness of our approach.
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Notes
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BCELoss\((x, y) = -(ylogx + (1 - y)log(1 - x))\).
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under grant No. 61872163 and 61806084, Jilin Province Key Scientific and Technological Research and Development Project under grant No. 20210201131GX, and Jilin Provincial Education Department project under grant No. JJKH20190160KJ.
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Wang, C., Peng, T., Zhang, Y., Yue, L., Liu, L. (2023). AOPSS: A Joint Learning Framework for Aspect-Opinion Pair Extraction as Semantic Segmentation. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_8
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