Mao Cunli

Question Classification Based on Incremental Modified Bayes

How to use the incremental training corpus to improve the question classification accuracy rate in the process of question classification based on statistic learning. A question classification method based on the incremental modified Bayes was presented in this paper. The method used the modified Bayes and combined the incremental learning to correct the parameter by the incremental training set stage by stage, and established the question classification model based on the incremental modified Bayes. A question classification experiment was done in the domain of Yunnan tourism, the experimental results showed that the presented method evidently excelled than the modified Bayes method in the accuracy rate and the training time, the average accuracy rate was improved 3.3 percentage points than the accuracy rate of the modified Bayes method; the average training time was improved 39.1 percentage points than the training time efficiency of the modified Bayes method.How to use the incremental training corpus to improve the question classification accuracy rate in the process of question classification based on statistic learning. A question classification method based on the incremental modified Bayes was presented in this paper. The method used the modified Bayes and combined the incremental learning to correct the parameter by the incremental training set stage by stage, and established the question classification model based on the incremental modified Bayes. A question classification experiment was done in the domain of Yunnan tourism, the experimental results showed that the presented method evidently excelled than the modified Bayes method in the accuracy rate and the training time, the average accuracy rate was improved 3.3 percentage points than the accuracy rate of the modified Bayes method; the average training time was improved 39.1 percentage points than the training time efficiency of the modified Bayes method.

A new algorithm based on word co-occurrence and its application in domain concept extraction

This paper puts forward an algorithm named CFE (Co-occurrence Frequency Emphasized) for new concept selection which based on word co-occurrence, it has been applied to domain concept extraction. This algorithm grasps the central idea of word co-occurrence rigidly. In the process of concept extracting, the co-occurrence frequency is taken as the basis of new concept selection, and the impact of absolute frequency to domain concept selection is taken into account. It is able to guarantee each candidate word will fairly get the opportunity of being selected as new concept. According to the data of the tests, a good result has been achieved.

The effects of domain knowledge relations on domain text classification

The text classification usually uses the statistical method to select characteristic. When it is carried out in different domains, the special interior knowledge relationships between domains will not be considered. In this paper, a new text classification model is proposed, which is based on the domain knowledge relations. This model adopts the support vector machine study algorithm, combine statistic samples and domain terminology to make up classification feature space, and calculate the similarity between domain conceptions, so that classification characteristic is entrusted with certain weight, realizing domain text classification. The new model has been made use of to carry out a text classification experiment about YunNan travel domain and non-travel domain. The result shows that domain knowledge has great effects on domain text classification and the accuracy of classification has been improved by 4 percentage compared with the improved TFIDF method.