Abstract
Existing studies on hierarchy constructionmainly focus on text corpora and indiscriminately mix numerous topics, thus increasing the possibility of knowledge acquisition bottlenecks and misconceptions. To address these problems and provide a comprehensive and in-depth representation of domain specific topics, we propose a novel topic hierarchy construction method with real-time update. This method combines heterogeneous evidence from multiple sources including folksonomy and encyclopedia, separately in both initial topic hierarchy construction and topic hierarchy improvement. Results of comprehensive experiments indicate that the proposed method significantly outperforms state-of-theart methods (t-test, p-value < 0.000 1); recall has particularly improved by 20.4% to 38.7%.
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Han Xue, PhD candidate in computer science, is a student at Harbin Institute of Technology, China. Her interests include information extraction and social computing.
Bing Qin, PhD in computer science, is a professor at the Department of Computer Science of Harbin Institute of Technology, China. Her interests include text mining and natural language processing.
Ting Liu, PhD in computer science, is a professor at the Department of Computer Science and Technology of Harbin Institute of Technology, China. His interests include information retrieval and social computing.
Shen Liu, MS candidate in computer science, is a student at Harbin Institute of Technology, China. His interests include information extraction and natural language processing.
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Xue, H., Qin, B., Liu, T. et al. Topic hierarchy construction from heterogeneous evidence. Front. Comput. Sci. 10, 136–146 (2016). https://doi.org/10.1007/s11704-015-4548-5
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DOI: https://doi.org/10.1007/s11704-015-4548-5