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As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news, however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures). Therefore, this study explores the lecture...
Feature selection is an important preprocessing step of Chinese Text Categorization, which reduces the high dimension and keeps the reduced results comprehensible compared to feature extraction. A novel criterion to filter features coarsely is proposed, which integrating the superiorities of term frequency-inverse document frequency as inner-class measure and CHI-square as inter-class, and a new feature...
This work proposes a hybrid model for text document classification for information retrieval using Naive Bayes and Rough set theory. Rough set theory is used for feature reduction and Naive Bayes theorem is used for classification of documents into the predefined categories by means of the probabilistic values. The deployment of the proposed model is planned through an enhanced method of the utilization...
The existence of vast unstructured text and the importance of the text information make the text mining technology be a hot research spot of Data Mining. Text classification is a very important subtask in the text mining. This paper focuses on the study of Chinese text classification based on single Chinese character feature. The experimental results indicate that the feature selection based on single...
Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. In this paper, we propose a new supervised feature selection method, named CHIR, which is based on the chi2 statistic and new statistical data that can measure the positive term-category dependency. We also propose a new text...
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