Open Science Research Excellence
@article{(International Science Index):,
  title    = {Online Topic Model for Broadcasting Contents Using Semantic Correlation Information},
  author    = {Chang-Uk Kwak and  Sun-Joong Kim and  Seong-Bae Park and  Sang-Jo Lee},
  country   = {Korea, Republic Of},
  institution={Kyungpook National University},
  abstract  = {This paper proposes a method of learning topics for
broadcasting contents. There are two kinds of texts related to
broadcasting contents. One is a broadcasting script, which is a series of
texts including directions and dialogues. The other is blogposts, which
possesses relatively abstracted contents, stories, and diverse
information of broadcasting contents. Although two texts range over
similar broadcasting contents, words in blogposts and broadcasting
script are different. When unseen words appear, it needs a method to
reflect to existing topic. In this paper, we introduce a semantic
vocabulary expansion method to reflect unseen words. We expand
topics of the broadcasting script by incorporating the words in
blogposts. Each word in blogposts is added to the most semantically
correlated topics. We use word2vec to get the semantic correlation
between words in blogposts and topics of scripts. The vocabularies of
topics are updated and then posterior inference is performed to
rearrange the topics. In experiments, we verified that the proposed
method can discover more salient topics for broadcasting contents.
    journal   = {International Journal of Computer, Electrical, Automation, Control and Information Engineering},  volume    = {10},
  number    = {1},
  year      = {2016},
  pages     = {77 - 80},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 109, 2016},