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 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 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.
Let p be an odd prime. For a kind of p-local co-H space Y of low-rank, it is shown in this paper that Ω Y has ΩΣ k Y as a product factor for a sequence of k which admits an exponential growth. The result appears as a result of the application of recently developed technique for functorial decompositions of looped co-H spaces via the functorial coalgebra decompositions of tensor algebras.
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