NDLI: An adaptive topic tracking approach based on Single-Pass clustering with sliding time window

Content Provider	IEEE Xplore Digital Library
Author	Gong Zhe Jia Zhe Luo Shoushan Tian Bin Niu Xinxin Xin Yang
Copyright Year	2011
Description	Author affiliation: Information Security Center, Beijing University of Posts and Telecommunications, China (Gong Zhe; Jia Zhe; Luo Shoushan; Tian Bin; Niu Xinxin; Xin Yang)
Abstract	Topic Tracking is one hot branch of Topic Detection and Tracking (TDT) research field. Due to the sparseness of initial corpus, the traditional topic tracking methods usually bring topic excursion into the system. The adaptive topic tracking methods are being put forward, but most of them use the fake feedback strategy so that the performance is not improved too much. In this paper, we proposed an adaptive topic tracking approach, which is based on an improved Single-Pass clustering algorithm with sliding time window. We present our own corpus preprocessing and feature weighting method to provide more exact vector space model (VSM) to next stage. In the topic tracking process, this paper uses a sliding time window strategy to guarantee the system accuracy and reduce the number of missed following stories. The experimental results show that our approach achieves satisfying results.
Starting Page	1311
Ending Page	1314
File Size	661960
Page Count	4
File Format	PDF
ISBN	9781457715860
e-ISBN	9781457715877
DOI	10.1109/ICCSNT.2011.6182201
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Single-Pass Clustering Lead Text Mining Adaptive Topic Tracking
Content Type	Text
Resource Type	Article

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