NDLI: Time adaptive boosting model for topic tracking

Content Provider	IEEE Xplore Digital Library
Author	Huizhen Wang Jingbo Zhu Duo Ji Na Ye Bin Zhang
Copyright Year	2005
Description	Author affiliation: Natural Language Process. Lab., Northeastern Univ., Shenyang, China (Huizhen Wang; Jingbo Zhu; Duo Ji; Na Ye)
Abstract	The technology of topic tracking can help people find what they are interested from the vast information sea. Since topics develop dynamically, topic excursion problem may appear in the tracking process. To overcome this problem and the shortcomings of current adaptive methods, we propose a new adaptive method for topic tracking. We call it time adaptive boosting (TAB) model. This model adopts the idea of boosting and presents new algorithm to the adaptive learning mechanism in the task of topic tracking. This algorithm can solve the problem of topic excursion, and remedy the deficiency of current adaptive methods. Time sequence of topic tracking task is also considered in the algorithm. We use sigmoid function to express it. In experiments we use the Chinese part in TDT4 corpus as test corpus, and use the TDT2004 evaluation metric to evaluate the adaptive Chinese topic tracking system based on TAB. Experimental results show that the adaptive method based on TAB can improve the performance of topic tracking.
Starting Page	488
Ending Page	492
File Size	1130765
Page Count	5
File Format	PDF
ISBN	0780393619
DOI	10.1109/NLPKE.2005.1598786
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems System testing Feedback Laboratories Computer applications Boosting Speech Natural language processing Software Floods
Content Type	Text
Resource Type	Article

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