A KGE Based Knowledge Enhancing Method for Aspect-Level Sentiment Classification

A KGE Based Knowledge Enhancing Method for Aspect-Level Sentiment Classification

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ALSC (Aspect-level Sentiment Classification) is a fine-grained task in the field of NLP (Natural Language Processing) which aims to identify the sentiment toward a given aspect.In addition to exploiting the sentence semantics redken shades eq 07m driftwood and syntax, current ALSC methods focus on introducing external knowledge as a supplementary to the sentence information.However, the integration of the three categories of information is still challenging.In this paper, a novel method is devised to effectively combine sufficient semantic and syntactic information as well as use of external knowledge.The proposed model contains a sentence encoder, a semantic learning here module, a syntax learning module, a knowledge enhancement module, an information fusion module and a sentiment classifier.

The semantic information and syntactic information are respectively extracted via a self-attention network and a graphical convolutional network.Specifically, the KGE (Knowledge Graph Embedding) is employed to enhance the feature representation of the aspect.Then, the attention-based gate mechanism is taken to fuse three types of information.We evaluated the proposed model on three benchmark datasets and the experimental results establish strong evidence of high accuracy.