title: Modification biterm topic model input feature for detecting topic in thematic virtual museums
metadata.name: dc.contributor.author
metadata.value: Anggai, Sajarwo
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Blekanov, Ivan S.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Sergeev, Sergei L.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2018-09-14T11:05:42Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2018-09-14T11:05:42Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2018-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Anggai S., Blekanov I. S., Sergeev S. L. Modification biterm topic model input feature for detecting topic in thematic virtual museums. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2018, vol. 14, iss. 3, pp. 243–251.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11702/spbu10.2018.305
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/14827
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: 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.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 14; Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: topic model
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: biterm
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: short text
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: BTM
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: clustering
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: thematic virtual museums
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Modification biterm topic model input feature for detecting topic in thematic virtual museums
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 05-Anggai.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/14827/1/05-Anggai.pdf
resources.format: Adobe PDF
resources.size: 429,12 kB