Linguistic attention-based model for aspect extraction

Yunjie Ji; Jie Li; Yanhua Yu

doi:10.1117/12.2513845

29 October 2018 Linguistic attention-based model for aspect extraction

Yunjie Ji, Jie Li, Yanhua Yu

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 1083619 (2018) https://doi.org/10.1117/12.2513845
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

Aspect extraction plays an important role in aspect-level sentiment analysis. Most existing approaches focus on explicit aspect extraction and either seriously rely on syntactic rules or only make use of neural network without linguistic knowledge. This paper proposes a linguistic attention-based model (LABM) to implement explicit and implicit aspect extraction together. The linguistic attention mechanism incorporates the knowledge of linguistics which has proven to be very useful in aspect extraction. We also propose a novel unsupervised training approach, distributed aspect learning (DAL), the core idea of DAL is that the aspect vector should align closely to the neural word embeddings of nouns which are tightly associated with the valid aspect indicators. Experimental results using six datasets demonstrate that our model is explainable and outperforms baseline models on evaluation tasks.

Citation Download Citation

Yunjie Ji, Jie Li, and Yanhua Yu "Linguistic attention-based model for aspect extraction", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083619 (29 October 2018); https://doi.org/10.1117/12.2513845

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