Abstract
Text categorization is one of the most common themes in data mining and machine learning fields. Unlike structured data, unstructured text data is more complicated to be analyzed because it contains too much information, e.g., syntactic and semantic. In this paper, we propose a semantics-based model to represent text data in two levels. One level is for syntactic information and the other is for semantic information. Syntactic level represents each document as a term vector, and the component records tf-idf value of each term. The semantic level represents document with Wikipedia concepts related to terms in syntactic level. The syntactic and semantic information are efficiently combined by our proposed multi-layer classification framework. Experimental results on benchmark dataset (Reuters-21578) have shown that the proposed representation model plus proposed classification framework improves the performance of text classification by comparing with the flat text representation models (term VSM, concept VSM, term+concept VSM) plus existing classification methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yates, R., Neto, B.: Modern information retrieval. Addison-Wesley Longman, Amsterdam (1999)
Jing, L., Zhou, L., Ng, M., Huang, J.: Ontology-based distance measure for text clustering. In: 4th Workshop on Text Mining, the 6th SDM, Bethesda, Maryland (2006)
Hotho, A., Staab, S., Stumme, G.: WordNet improves text document clustering. In: The Semantic web workshop at the 26th ACM SIGIR, Toronto, Canada, pp. 541–544 (2003)
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: The 20th IJCAI, Hyderabad, India, pp. 1606–1611 (2007)
Gabrilovich, E., Markovitch, S.: Wikipedia-based semantic interpretation for natural language processing. J. of Artificial Intelligence Research 34, 443–498 (2009)
Wang, P., Domeniconi, C.: Building semantic kernels for text classification using Wikipedia. In: The 14th ACM SIGKDD, New York, pp. 713–721 (2008)
Hu, J., Fang, L., Cao, Y., Zeng, H., Li, H., Yang, Q., Chen, Z.: Enhancing text clustering by leveraging Wikipedia semantics. In: The 31st ACM SIGIR, Singapore, pp. 179–186 (2008)
Huang, A., Milne, D., Frank, E., Witten, I.: Clustering documents using a Wikipedia-based concept representation. In: The 13rd PAKDD, Bangkok, Thailand, pp. 628–636 (2009)
Hu, X., Zhang, X., Lu, C., Park, E.K., Zhou, X.: Exploiting Wikipedia as External Knowledge for Document Clustering. In: The 15th ACM SIGKDD, Paris, pp. 389–396 (2009)
Chow, T., Rahman, M.: Multilayer som with tree-structured data for efficient document retrieval and plagiarism detection. IEEE Trans. on Neural Networks 20, 1385–1402 (2009)
Medelyan, O., Witten, I., Milne, D.: Topic indexing with wikipedia. In: The AAAI Wikipedia and AI workshop, Chicago (2008)
Milne, D., Witten, I.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: The Workshop on Wikipedia and Artificial Intelligence at AAAI, Chicago, pp. 25–30 (2008)
Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-trainin. In: The 9th CIKM, New York, pp. 86–93 (2000)
Scholkopf, B., Burges, C., Smola, A.: Advances in kernel methods: support vector learning. MIT Press, Cambridge (1999)
Shakhnarovich, G., Darrell, T., Indyk, P.: Nearest-Neighbor methods in learning and vision. The MIT Press, Cambridge (2005)
Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, Cambridge (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yun, J., Jing, L., Yu, J., Huang, H. (2010). Semantics-Based Representation Model for Multi-layer Text Classification. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15390-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-15390-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15389-1
Online ISBN: 978-3-642-15390-7
eBook Packages: Computer ScienceComputer Science (R0)