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Computer science
Modification biterm topic model input feature for detecting topic in thematic virtual museums
S. Anggaia, I. S. Blekanov, S. L. Sergeev a St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg,
199034, Russian Federation
Abstract:
This paper describes the method for detecting topic in short text documents developed by the authors. The method called Feature BTM, based on the modification of the third step of the generative process of the well-known BTM model. The authors conducted experiments of quality evaluation that have shown the advantage of efficiency by the modified Feature BTM model before the Standard BTM model. The thematic clustering technology of documents necessary for the creation of thematic virtual museums has described. The authors performed a performance evaluation that shows a slight loss of speed (less than 30 seconds), more effective using the Feature-BTM for clustering the virtual museum collection than the Standard BTM model.
Keywords:
topic model, biterm, short text, BTM, clustering, thematic virtual museums.
Received: March 10, 2018 Accepted: June 14, 2018
Citation:
S. Anggai, I. S. Blekanov, S. L. Sergeev, “Modification biterm topic model input feature for detecting topic in thematic virtual museums”, Vestnik S.-Petersburg Univ. Ser. 10. Prikl. Mat. Inform. Prots. Upr., 14:3 (2018), 243–251
Linking options:
https://www.mathnet.ru/eng/vspui373 https://www.mathnet.ru/eng/vspui/v14/i3/p243
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Abstract page: | 122 | Full-text PDF : | 40 | References: | 39 | First page: | 3 |
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