Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8960109 | International Journal of Approximate Reasoning | 2018 | 10 Pages |
Abstract
Representing words as real-value vectors and use them as input in deep neural networks is an effective approach in many natural language processing tasks. Currently, some studies use a lower-level representation which is character-based vectors. This paper addresses on how to integrate different representations of input for the problem of aspect-based sentiment analysis. We will propose a joint model of multiple Convolutional Neural Networks (CNNs) in which each individual representation of the input is handled by one CNN. In this work we focus on three kinds of representation including word embeddings from the two methods (Word2Vec and GloVe) and the one-hot character vectors. Our experimental results demonstrate that the proposed model can achieve state-of the-art performance in aspect category detection and aspect sentiment classification tasks.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Duc-Hong Pham, Anh-Cuong Le,