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Nowadays, text classification (TC) becomes the main applications of NLP (natural language processing). Actually, we have a lot of researches in classifying text documents, such as Random Forest, Support Vector Machines and Naive Bayes. However, most of them are applied for English documents. Therefore, the text classification researches on Vietnamese still are limited. By using a Vietnamese news corpus,...
Text classification (TC) is a task that assigns a text to one or more classes and predefined categories. Constructing text classifiers with high accuracy is a vital task in biomedical field, given the wealth of information hidden in unlabelled documents. Because of large feature spaces, traditionally discriminative approaches, such as logistic regression and support vector machines with n-gram and...
Since short text is characterized of the short length, sparse features and strong context dependency, the traditional models have a limited precision. Motivated by this, this article offers an empirical exploration on a character-level model which implements a combination of convolutional neural network(CNN) and recurrent neural networks(RNN) for short text classification. Including the highway networks...
Nowadays, large volumes of text data are being produced in real time due to expansion of communication. It is necessary to organize this data for exploitation and extraction of useful information. Text classification based on the topic is one of the efficient solutions to this problem. Efficient algorithms are applied for text classification if they address high dimensional data. In this paper, a...
Spoken language understanding (SLU) is one of the important problem in natural language processing, and especially in dialog system. Fifth Dialog State Tracking Challenge (DSTC5) introduced a SLU challenge task, which is automatic tagging to speech utterances by two speaker roles with speech acts tag and semantic slots tag. In this paper, we focus on speech acts tagging. We propose local coactivate...
This research proposes an approach for text classification that uses a simple neural network called Dynamic Text Classifier Neural Network (DTCNN). The neural network uses as input vectors of words with variable dimension without information loss called Dynamic Token Vectors (DTV). The proposed neural network is designed for the classification of large and short text into categories. The learning...
Classification systems adapts many machine learning techniques for quality performance in data classification. The neural networks has some unique characteristics and features which can handle high dimensional features and documents with noise and contradictory data. Classification is important to classify the input text into different domains appropriately. This paper give out a move towards classification...
In this paper we take our effort to achieve a fast and accurate classifier: a BVB (BAM-Vote Box)-based framework is presented for text categorization by using ensemble method. The central idea is that combining two-class classifications for the multi-class tasks. This framework generates associating terms and extending the set of basis element, and includes a feature selection method, which can reduce...
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