The traditional KNN text classification algorithm used all training samples for classification, so it had a huge number of training samples and a high degree of calculation complexity, and it also didn’t reflect the different importance of different samples. In allusion to the problems mentioned above, an improved KNN text classification algorithm based on clustering center is proposed in this paper. Firstly, the given training sets are compressed and the samples near by the border are deleted, so the multipeak effect of the training sample sets is eliminated. Secondly, the training sample sets of each category are clustered by k-means clustering algorithm, and all cluster centers are taken as the new training samples. Thirdly, a weight value is introduced, which indicates the importance of each training sample according to the number of samples in the cluster that contains this cluster center. Finally, the modified samples are used to accomplish KNN text classification. The simulation results show that the algorithm proposed in this paper can not only effectively reduce the actual number of training samples and lower the calculation complexity, but also improve the accuracy of KNN text classification algorithm.

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An Improved KNN Text Classification Algorithm Based on Clustering

New text categorization models using back-propagation neural network (BPNN) and modified back-propagation neural network (MBPNN) are proposed. An efficient feature selection method is used to reduce the dimensionality as well as improve the performance. The basic BPNN learning algorithm has the drawback of slow training speed, so we modify the basic BPNN learning algorithm to accelerate the training speed. The categorization accuracy also has been improved consequently. Traditional word-matching based text categorization system uses vector space model (VSM) to represent the document. However, it needs a high dimensional space to represent the document, and does not take into account the semantic relationship between terms, which can also lead to poor classification accuracy. Latent semantic analysis (LSA) can overcome the problems caused by using statistically derived conceptual indices instead of individual words. It constructs a conceptual vector space in which each term or document is represented as a vector in the space. It not only greatly reduces the dimensionality but also discovers the important associative relationship between terms. We test our categorization models on 20-newsgroup data set, experimental results show that the models using MBPNN outperform than the basic BPNN. And the application of LSA for our system can lead to dramatic dimensionality reduction while achieving good classification results.

Latent semantic analysis for text categorization using neural network

In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.

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Advances in Machine Learning Based Text Categorization

Chinese question classification is one of the essential tasks in nature language processing (NLP) for Chinese language due to its distinctive characteristics. Methods presented in the literature are usually based on rules or traditional machine learning methods, which require manually created rules or features. Thus, the accuracy of the classification is constrained by inherent limitations of these methods. As deep learning-based methods have been proved to be able to mine deep information of text, to alleviate the problem, this article proposes a novel deep neural network model, Attention-Based BiGRU-CNN network (ABBC); and applies it to Chinese question classification task. The model combines the characteristics and advantages of convolutional neural network, attention mechanism and recurrent neural network. Our model can not only extract the features of Chinese questions effectively, but also learn the context information of words to solve the problem that the Text-CNN model can lose position feature. By comparing out model to four other classic models, the experimental results show that our model achieves the best performance in the Chinese question classification task.

Attention-based BiGRU-CNN for Chinese question classification

Text classification is a classic task in the field of natural language processing. however, the existing methods of text classification tasks still need to be improved because of the complex abstraction of text semantic information and the strong relecvance of context. In this paper, we combine the advantages of two traditional neural network model, Long Short-Term Memory(LSTM) and Convolutional Neural Network(CNN). LSTM can effectively preserve the characteristics of historical information in long text sequences, and extract local features of text by using the structure of CNN. We proposes a hybrid model of LSTM and CNN, construct CNN model on the top of LSTM, the text feature vector output from LSTM is further extracted by CNN structure. The performance of the hybrid model is compared with that of other models in the experiment. The experimental results show that the hybrid model can effectively improve the accuracy of text classification.

Wang Jianhui

Papers

Using Maximum Entropy Model for Chinese Text Categorization

A Simple and Efficient Algorithm to Classify a Large Scale of Texts