Abstract
Aspect term extraction (ATE) is to extract explicit aspect expressions from online reviews. This paper focused on the supervised extraction of aspect term. Previous models for ATE either ignored the opinion information or improperly utilized the opinion information with a high-coupling method. We proposed a model to perform ATE with the assistance of opinion knowledge, called opinion knowledge injection network. Specifically, the proposed model distills the opinion knowledge through the attention mechanism and joins it into each word to assist aspect extraction. The proposed model achieved surprisingly good results, improving 1.34% and 1.23% than the best results before respectively on the laptop and restaurant datasets, and reached state-of-the-art.
S. Zhang–This work was supported in part by the National key research and development program of China(2016QY01W0200). Research Innovation Fund for College Students of Beijing University of Posts and Telecommunications. National Key Research and Development Program of China (2017YFB1400603).
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Zhang, S., Lu, G., Shuang, K. (2019). Opinion Knowledge Injection Network for Aspect Extraction. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_56
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