Phoneme Based Representation for Vietnamese Web Page Classification

This paper proposes a novel text representation for Web pages written in Vietnamese. This representation is based on an analysis of Vietnamese documents at phonetic level in which each document will be represented as a bag of phonemes. It is designed to capture sound-based information in documents and to be helpful for resolving some non-topic text classification problems including automatic Vietnamese language identification of a document, ancient Vietnamese document detection, author identification, and poem identification. We apply some typical machine learning methods including NB, KNN and SVMs to build text classifiers. The experimental results show a significant improvement in terms of effectiveness and efficiency compared to the traditional syllable based representation in most cases.