Fine-grained sentiment polarity classification for short texts has been an important and challenging task in natural language processing until these years. The short texts may contain multiple aspect-terms, opinion terms expressing different sentiments for different aspect-terms. The polarity of the whole sentence is highly correlated with the aspect-terms and opinion terms. Besides, there are two challenges, which are how to effectively use the contextual information and the semantic features, and how to model the correlations between aspect-terms and context words including opinion terms. To solve these problems, a Self-Attention-Based BiLSTM model with aspect-term information is proposed for the fine-grained sentiment polarity classification for short texts. The proposed model can effectively use contextual information and semantic features, and especially model the correlations between aspect-terms and context words. The model mainly consists of a word-encode layer, a BiLSTM layer, a self-attention layer and a softmax layer. Among them, the BiLSTM layer sums up the information from two opposite directions of a sentence through two independent LSTMs. The self-attention layer captures the more important parts of a sentence when different aspect-terms are input. Between the BiLSTM layer and the self-attention layer, the hidden vector and the aspect-term vector are fused by adding, which reduces the computational complexity caused by the vector splicing directly. The experiments on public Restaurant and Laptop corpus from the SemEval 2014 Task 4, and Twitter corpus from the ACL 14. The Friedman and Nemenyi tests are used in the comparison study. Compared with existing methods, experimental results demonstrate that the proposed model is feasible and efficient. INDEX TERMS Aspect-term, bidirectional LSTM (BiLSTM), fine-grained, self-attention.

This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed to extract the image feature information. Based on the back propagation (BP) neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, although there are complex textures and illumination variations on the images of the face database named AT&T, the detection accuracy rate of the proposed method can reach above 0.93. In addition, extensive simulations based on the Yale and extended Yale B datasets further verify the effectiveness of the proposed method.

Abstract:To overcome the limitation of artificial judgment of meibomian gland morphology, we proposed a solution based on an improved fuzzy c-means (FCM) algorithm and rough sets theory. The rough sets reduced the redundant attributes while ensuring classification accuracy, and greatly reduced the amount of computation to achieve information dimension compression and knowledge system simplification. However, before this reduction, data must be discretized, and this process causes some degree of information loss. Therefore, to maintain the integrity of the information, we used the improved FCM to make attributes fuzzy instead of discrete before continuing with attribute reduction, and thus, the implicit knowledge and decision rules were more accurate. Our algorithm overcame the defects of the traditional FCM algorithm, which is sensitive to outliers and easily falls into local optima. Our experimental results show that the proposed method improved recognition efficiency without degrading recognition accuracy, which was as high as 97.5%. Furthermore, the meibomian gland morphology was diagnosed efficiently, and thus this method can provide practical application values for the recognition of meibomian gland morphology.

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