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Efficient Classification of Noisy Text

Mita K. Dalal


Textual content comprises a significant volume of data generated online on a daily basis. The web-generated data often consists of high levels of noise due to a variety of factors.  Development of efficient systems for automatic classification of noisy data is a crucial task in text mining. This paper examines a technique for classification of noisy text which is based on multiple feature selection and supervised learning. The main aim of the paper is to examine the efficiency of the text classification approach against increasing levels of word error rate, using both standard and web-based data sets.  Empirical evaluation of the classification approach indicates that it is efficient and reliable in the presence of noise.


text classification, lexical noise, machine learning, naïve Bayesian classification, acronym expansion, feature selection

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