Topic tracking using subject templates
نویسندگان
چکیده
Topic tracking, which starts from a few sample stories and finds all subsequent stories that discuss the same topic, is a new challenge for the text categorization task and makes a significant contribution to the accessibility of information, such as archives of news, e-mails, and historical newspapers. Much previous research on topic tracking uses machine learning techniques. However, the small size of the training data, especially positive training stories, presents difficulties in training the parameters of the tracking system to produce optimal results. In this paper, we present a method for topic tracking using subject templates to select an optimal training set. The method was tested on the TV news which are the outputs of a speech recognizer, and the result shows the effectiveness of the method.
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