Legal Case Document Classification Application Based on an Improved Hybrid Approach

Abstract

The automatic classification of legal case documents has become very important, owing to the justice denials, delays and failures observed in the judicial case management systems. Our hybrid text classification model employed extensive preprocessing techniques to prepare the document features, the probabilistic nature of the Naïve Bayes algorithm was integrated to generate vectorized data from the document features for the classifier, and the most important features was selected by feature ranking using the Chi Square method for final classification using the Support Vector Machine. The hybrid text classifier application was implemented using the Object Oriented Analysis and Design Methodology and developed using the Java programming language and MySQL. Results showed that best features were selected and the documents were accurately classified to their right categories using this hybrid application, as proven using standard performance measure metrics.

Improved Hybrid Model for Classification of Text Documents

“…Fig. 1 shows an extended text document classification architecture adopted from Ugwu and Obasi [16]. The table extractor component was used to extract text from the tables which were used to hold the data in the documents.…”

An Improved Model for Legal Case Text Document Classification

“…Fig. 1 shows an extension of the Text Document Classification architecture adopted by Ugwu and Obasi [15].…”

“…A legal case document classification system was developed using a hybrid approach [15]. The architecture of the system integrates stages that include document collection, document pre-processing, dimensionality reduction, vectorization, training, and classification.…”