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With rapid development of E-commerce platforms, automated review sentiment analysis for commodities becomes a research focus, with main purpose to extract potential information within reviews for decision making of consumers. Traditional methods have made some progress on document level sentiment analysis, but with tremendous increasing of data scale, how to process high dimension of data fast and...
Related research for sentiment analysis on Chinese microblog is aiming at analyzing the emotion of posters. This paper presents a content extension method that combines post with its' comments into a microblog conversation for sentiment analysis. A new convolutional auto encoder which can extract contextual sentiment information from microblog conversation of the post is proposed. Furthermore, a DBN...
This work explores Deep Belief Networks (DBN) for the task of detecting Vowel-like regions (VLRs). Vowels and semivowels are considered as VLRs. By using vocal tract features at the input layer of DBN, we extract an evidence for VLRs by transforming the vocal tract features through multiple non-linear hidden layers. The linear classifier is used to predict the class of evidence, i.e.,whether it is...
This paper proposes a new composition method to represent semantic compositionality of sentences. Using the unfolding recursive autoencoders, we build sentence representing trees from the original sentences of words. We utilize trained word embeddings and sentence parser to train the model, and we can build sentence representing trees from the trained model. We further propose to use dynamic average...
In the context of handwritten mathematical expressions recognition, a first step consist on grouping strokes (segmentation) to form symbol hypotheses: groups of strokes that might represent a symbol. Then, the symbol recognition step needs to cope with the identification of wrong segmented symbols (false hypotheses). However, previous works on symbol recognition consider only correctly segmented symbols...
It is known that convolutional neural networks (CNNs) are efficient for optical character recognition (OCR) and many other visual classification tasks. This paper applies error-correcting output coding (ECOC) to the CNN for segmentation-free OCR such that: 1) the CNN target outputs are designed according to code words of length N; 2) the minimum Hamming distance of the code words is designed to be...
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