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Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective model for topics representation. On the basis of K-nearest neighbor (KNN) algorithm for text classification and support vector machines (SVM) algorithm for text classification, we have studied how they affect topic tracking. Then we get the variation law that they affect...
Conference web pages display their topics information in different ways, and conferences in different domains accept papers on different topics. Automatic extraction of topics information from conference web pages is thus a difficult task and has not received much attention from the research community. In this paper, we propose a method for extracting topics information that uses a web page segmentation...
One of the main preprocessing steps for having a high performance text classifier is feature weighting. Commonly used feature weighting methods such as TF and IDF-based methods only consider the distribution of a feature in the document(s) and do not consider class information for feature weighting. In this paper, we present TFCRF (Term Frequency and Category Relevancy Factor) method in which the...
Topic tracking is to track trend of news topic, which people are interested in. It is a very pragmatic method in information retrieval. Compared with keywords retrieval, topic tracking excels in dynamic tracking based on text model and its content understanding, so it is mostly involved in text expressing and semantic understanding. LS-SVM, as a new method for news topic tracking, is presented in...
Traditional text learning algorithms need labeled documents to supervise the learning process, but labeling documents of a specific class is often expensive and time consuming. We observe it is convenient to use some keywords(i.e. class-descriptions) to describe class sometimes. However, short class-description usually does not contain enough information to guide classification. Fortunately, large...
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