Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis

Fei Liu; Trevor Cohn; Timothy Baldwin

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arXiv · 1804.11019

Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis

Fei Liu·Trevor Cohn·Timothy Baldwin

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Abstract

While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects --- remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel architecture, utilising external "memory chains" with a delayed memory update mechanism to track entities. On a TABSA task, the proposed model demonstrates substantial improvements over state-of-the-art approaches, including those using external knowledge bases.

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BibTeX RIS

Fei Liu, Trevor Cohn, Timothy Baldwin. 2018-04-30. Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis. https://arxiv.org/abs/1804.11019

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