Summary of the paper

Title Improving K-Nearest Neighbor Efficacy for Farsi Text Classification
Authors Mohammad Hossein Elahimanesh, Behrouz Minaei and Hossein Malekinezhad
Abstract One of the common processes in the field of text mining is text classification. Because of the complex nature of Farsi language, words with separate parts and combined verbs, the most of text classification systems are not applicable to Farsi texts. K-Nearest Neighbors (KNN) is one of the most popular used methods for text classification and presents good performance in experiments on different datasets. A method to improve the classification performance of KNN is proposed in this paper. Effects of removing or maintaining stop words, applying N-Grams with different lengths are also studied. For this study, a portion of a standard Farsi corpus called Hamshahri1 and articles of some archived newspapers are used. As the results indicate, classification efficiency improves by applying this approach especially when eight-grams indexing method and removing stop words are applied. Using N-grams with lengths more than 3 characters, presented very encouraging results for Farsi text classification. The Results of classification using our method are compared with the results obtained by mentioned related works.
Topics Text mining, Evaluation methodologies, Statistical and machine learning methods
Full paper Improving K-Nearest Neighbor Efficacy for Farsi Text Classification
Bibtex @InProceedings{ELAHIMANESH12.903,
  author = {Mohammad Hossein Elahimanesh and Behrouz Minaei and Hossein Malekinezhad},
  title = {Improving K-Nearest Neighbor Efficacy for Farsi Text Classification},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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