1School of Information Science & Engineering, Yunnan University, Kunming 650500, China 2School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China 3Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service,Chengdu University of Information Technology, Chengdu 610225, China
[Objective] This paper modifies the topic model to improve the quality of extracted news clues. [Methods] We constructed a News-IBTM model based on IBTM (Incremental Biterm Topic Model) with dynamic sliding window, which reduced the extraction scope of binary phrases. Then, we used this model to extract topics and topic-word distributions from news, and inferred the document-topic distributions. Finally, we used the JS (Jensen-Shannon) divergence to measure the difference between document-topic distributions and generate news clues. [Results] We examined our News-IBTM model with news from People’s Daily Online and Weibo. The proposed model outperformed existing ones in perplexity, accuracy and efficiency. [Limitations] The accuracy of News-IBTM algorithm needs to be further improved. [Conclusions] The proposed method could effectively extract quality news topics and clues.
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