Research on classifier design of Chinese abstracts of postgraduate dissertations in library collection

Abstract

At present, there are many researches and application fields of Chinese text classification, such as news classification, emotion classification, and spam identification and so on [1]. However, there are relatively few researches on the classification of Chinese abstracts of postgraduate dissertations. The abstract of postgraduate dissertation is a highly summary of the purpose, method, innovation, key and difficult points and results of the whole dissertation’s research. To recognize the abstracts of postgraduate dissertations in the library collection, and then to classify the dissertations in the library collection, is beneficial to the majority of teachers and students to consult the dissertations efficiently, and can better recommend the dissertations to readers according to their research background. Therefore, how to automatically classify postgraduate dissertations in library collection is an urgent problem to deal with. Assume that the abstract of postgraduate dissertation is written strictly according to the writing requirements, therefore, the abstract of the dissertation can represent the classification of the research field for the whole dissertation. Extract the abstracts of the dissertations as characteristic data X, label data Y was made based on the information of the school where the authors of the dissertations work in, based on the postgraduate dissertations collected in the library of the university. The dissertations studied were divided into eight categories, So Y can take integer values from one to eight. After preprocessing, the word vector and classifier were trained by the way of machine learning [2]. The results show that the classifiers can output the classification more accurately when a new abstract is randomly input to the models that has been trained.