A note on document classification with small training data

Yasunari Maeda, Hideki Yoshida, Masakiyo Suzuki, Toshiyasu Matsushima

Research output: Contribution to journalArticle

Abstract

Document classification is one of important topics in the field of NLP (Natural Language Processing). In the previous research a document classification method has been proposed which minimizes an error rate with reference to a Bayes criterion. But when the number of documents in training data is small, the accuracy of the previous method is low. So in this research we use estimating data in order to estimate prior distributions. When the training data is small the accuracy using estimating data is higher than the accuracy of the previous method. But when the training data is big the accuracy using estimating data is lower than the accuracy of the previous method. So in this research we also propose another technique whose accuracy is higher than the accuracy of the previous method when the training data is small, and is almost the same as the accuracy of the previous method when the training data is big.

Original languageEnglish
Pages (from-to)1459-1466
Number of pages8
JournalIEEJ Transactions on Electronics, Information and Systems
Volume131
Issue number8
DOIs
Publication statusPublished - 2011

Keywords

  • Document classification
  • Posterior distribution
  • Prior distribution
  • Training data

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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