Feature Selection And Vectorization In Legal Case DocumentsUsing Chi-Square Statistical Analysis And Naïve BayesApproaches
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 2
Abstract
Abstract : Most machine learning techniques employed in the area of text classification require the features ofthe documents to be effectively selected owing to the large chunk of data encountered in the classificationprocess and term weights built from document vectors for proper infusing into the respective classifieralgorithms. Effective selection of the most important features from the raw documents is achieved byimplementing more extensive pre-processing techniques and the features obtained were ranked using the chisquarestatistical approach for the elimination of irrelevant features and proper selection of more relevantfeatures in the entire corpus. The most relevant ranked features obtained are converted to word vectors which isbased on the number of occurrences of words in the documents or categories concerned, using the probabilisticcharacteristics of Naïve Bayes as a vectorizer for machine learning classifiers. This hybrid vector space modelwas experimented on legal text categories and the study revealed better discovered features using the preprocessingand ranking technique, while better term weights from the documents was successfully built formachine learning classifiers used in the text classification process.
Authors and Affiliations
Obasi, Chinedu Kingsley , Ugwu, Chidiebere
Keywords
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