Abstract
Automatic classification of Chinese text documents requires a machine to process and analyze the meaning of Chinese terms. We propose an Ontology Graph based approach to measure the relations between Chinese terms for the text classification purpose. The method improves traditional high dimensional termbased text classification approach, in that the new method selects very small number of semantically related concepts to create Ontology Graphs. The Ontology Graphs can be used to represent different classes (domains). It enhances text classification performance by using its small-size but high semantically associated concepts. Our experiments show that the proposed method has classified a Chinese document set with 92% accuracy in f-measure by using Ontology Graphs containing only 80 concepts for each class. The high accuracy result shows that the Ontology Graphs used in the process are enable to represent the knowledge of a domain and also the Ontology Graph based approach of text classification is effective and accurate.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lim, E.H.Y., Liu, J.N.K., Lee, R.S.T. (2011). Ontology Graph Based Approach for Automatic Chinese Text Classification. In: Knowledge Seeker - Ontology Modelling for Information Search and Management. Intelligent Systems Reference Library, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17916-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-17916-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17915-0
Online ISBN: 978-3-642-17916-7
eBook Packages: EngineeringEngineering (R0)