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E.A. Aziz
Faculty of Language and Arts Education, Indonesia University of Education, Indonesia
Computer Science & IT

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Free Model of Sentence Classifier for Automatic Extraction of Topic Sentences M.L. Khodra; D.H. Widyantoro; E.A. Aziz; B.R. Trilaksono
Journal of ICT Research and Applications Vol. 5 No. 1 (2011)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.2011.5.1.2

Abstract

This  research  employs  free  model  that  uses  only  sentential  features without paragraph context  to extract topic sentences of a paragraph. For finding optimal  combination  of  features,  corpus-based  classification  is  used  for constructing a sentence classifier  as the model.  The sentence classifier is trained by  using Support Vector Machine  (SVM).  The experiment shows that position and meta-discourse features are more important  than syntactic features  to extract topic  sentence,  and  the  best  performer  (80.68%)  is  SVM  classifier  with  all features. 
Co-Authors B.R. Trilaksono D.H. Widyantoro M.L. Khodra