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Creators/Authors contains: "Rahimi, R."

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  1. ; ; ; (, MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture)
    Full Text Available 
  2. ; ; (, Proceedings of ACM SIGIR Workshop on eCommerce (SIGIR eCom’20))
    null (Ed.)
    Aspect category detection (ACD) is one of the challenging sub-tasks in aspect-based sentiment analysis. The goal of this task is to detect implicit or explicit aspect categories from the sentences of user-generated reviews. Since annotation over the aspects is time-consuming, the amount of labeled data is limited for super-vised learning. In this paper, we study contextual representations of reviews using the BERT model to better extract useful features from text segments in the reviews, and train a supervised classifier with a small amount of labeled data for the ACD task. Experimental results obtained on Amazon reviews of six product domains show that our method is effective in some domains. 
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    Accepted Manuscript Full Text Available