Study on Classification Technology of Tibetan Text

This paper gives an overview of the present situation of Tibetan text classification study. This paper analyzes and compares today's latest representative Tibetan text classification algorithms and summarizes the various characteristics of each algorithm. So it enables researchers to improve the existing algorithms and put forward new Tibetan text classification algorithms with higher performance. Meanwhile, it is convenient for users to select and use the algorithm in application. Introduction Classification is a category of technology, which is based on data set to construct a classifier. Meanwhile, the classifiers is useful for unknown categories of samples. The basic method of text classification is based on some characteristics of the data to be classified to match. It is not possible to match completely. Therefore, we need to filter the matching result to choose the most excellent match, so as to achieve classification. Text classification methods from the beginning of the word matching method and the subsequent emergence of knowledge engineering methods to today's statistical learning methods. The classification technology is more and more mature. The statistical learning method needs some already classified text as the training set. Then the computer extracts the effective classification rules from the training and learning. Finally the set of these rules is used as the classifier. After the training is finished, the text to be classified is classified using the trained classifier. Before the classification of Tibetan text, it is necessary to convert the Tibetan text into information that the computer can recognize. The most commonly used text representation method is the vector space model. In this model, the text space is regarded as a set of orthogonal eigenvectors vector space. Tibetan text vector space model is a representation of any Tibetan text in the form of space vector. And take the feature item as the basic unit of Tibetan text representation. Each dimension of the vector corresponds to a feature item in the Tibetan text. Each dimension itself represents the weight of the corresponding feature item in the Tibetan text. The weights represent the importance of the feature item to the Tibetan text, and reflect the ability of the feature item to reflect the content of the Tibetan text. The common methods of feature extraction include document frequency, information gain, mutual information, CHI square statistics, expected cross entropy and so on. The common feature weight calculation methods are: TF function, TF-IDF function and Boolean functions. Tibetan text classification process shown in figure 1. Foreword Domestic research on Tibetan information processing technology started in the 1980s [1]. Nowadays, with the rapid increase of Tibetan electronic text data, in order to classify Tibetan text content accurately, Tibetan text classification technology has been developed gradually. At the same time, Tibetan text classification algorithm research is also an important part of Tibetan public opinion analysis. Because the Tibetan information processing technology is lagging behind, and lack of the Tibetan classification tagged corpus. Up to now, the Tibetan text classification technology has not yet been in-depth study and discussion. More representative studies include: Xu Gui Xian, who use web page