Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 200-206    DOI: 10.11925/infotech.2096-3467.2019.0634
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Tracking Static Topics with Bayesian Network
Xu Jianmin(),Zhang Liqing,Wang Miao
School of Cyber Security and Computer, Hebei University, Baoding 071002, China
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Abstract

[Objective] The paper analyzed the feasibility of using Bayesian network for topic tracking, and proposed a new method to improve its performance.[Methods] We constructed two topic tracking models, one with Bayesian Network, and the other with Extended Bayesian Network. The nodes in the models represent terms, events and topics, while the arcs represent relationships among nodes. Finally, we calculated the similarity among topics, events and reports with the Propagation and Evaluation method.[Results] We examined our models on TDT4 data set and found the DET curve of the Bayesian Network model was below the curve of vector space topic model, the former had better performance. The result of extended Bayesian network topic tracking model was 1.7% higher than the first one.[Limitations] Extended Bayesian network topic tracking model was a static topic model while events were generated by the evolution of topics, so the model had limited performance improvement.[Conclusions] The new models can describe the structural relationships among topics, events and stories, and conduct probability inference, which improve the performance of topic tracking effectively.

Received: 10 June 2019      Published: 26 April 2020
 ZTFLH: TP391.1
Corresponding Authors: Jianmin Xu     E-mail: hbuxjm@hbu.edu.cn
 Bayesian Network BNTT Model E_BNTT Model Parameters Description Performance of E_BNTT Model with Different Values of Parameter$δ$ Performance of BNTT and VSM Performance of BNTT and E_BNTT