MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews
International Joint Conference on Natural Language ProcessingPublished 1 November 2017
Wei Xue, Wubai Zhou, Tao Li, Qing Wang
Citations53
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TL;DR
Improved performance of the multi-task learning model based on neural networks over the models trained separately on three public dataset released by SemEval workshops is demonstrated.
Abstract
Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities. In Aspect Based Sentiment Analysis (ABSA), it is critical to identify aspect categories and extract aspect terms from the sentences of user-generated reviews. However, the two tasks are often treated independently, even though they are closely related. Intuitively, the learned knowledge of one task should inform the other learning task. In this paper, we propose a multi-task learning model based on neural networks to solve them together. We demonstrate the improved performance of our multi-task learning model over the models trained separately on three public dataset released by SemEval workshops.
Keywords
Computer Science
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