Language identification based on n-gram feature extraction method by using classifiers

Abstract

The rising opportunities of communication provided us with many documents in many different languages. Language identification has a key role for these documents to be understandable and to study natural language identification procedures. The increasing number of the documents and international communication requirements make new works on language identification obligatory. Until today, there have been a great number of studies on solving language identification problem about document based language identification. In these studies, characters, words and n-gram sequences have been used with machine learning techniques. In this study, sequence of n-gram frequencies will be used and using of the five different classification algorithms' accuracy performances will be analyzed via different sizes of documents belonging to 15 different languages. N-gram based feature method will be used to extract feature vector belonging to languages. The most appropriate method for the problem of language identification will be identified by comparing the performances of the Support Vector Machines, Multilayer Perceptron, Centroid Classifier, k-Means and Fuzzy C Means methods. During the experiments, trainining and testing data will be selected from ECI multilingual corpus.