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2016 | OriginalPaper | Chapter

Effectively Classifying Short Texts via Improved Lexical Category and Semantic Features

Authors : Huifang Ma, Runan Zhou, Fang Liu, Xiaoyong Lu

Published in: Intelligent Computing Theories and Application

Publisher: Springer International Publishing

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Abstract

Classification of short text is challenging due to its severe sparseness and high dimension, which are typical characteristics of short text. In this paper, we propose a novel approach to classify short texts based on both lexical and semantic features. Firstly, the term dictionary is constructed by selecting lexical features that are most representative words of a certain category, and then the optimal topic distribution from the background knowledge repository is extracted via Latent Dirichlet Allocation. The new feature for short text is thereafter constructed. The experimental results show that our method achieved significant quality enhancement in terms of short text classification.

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Literature
1.
Gupta, V., Lehal, G.S.: A survey of text mining techniques and applications. J. Emerg. Technol. Web Intell. 1(1), 60–76 (2009)
2.
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRef
3.
Cheng, Q.Q., Wang, L.L., Zheng, T., et al.: Microblog friend recommendation based on multi-feature classification. Comput. Eng. 41(4), 65–69 (2015)
4.
Sun, A.: Short text classification using very few words. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, USA, pp. 1145–1146 (2012)
5.
Vo, D.T., Ock, C.Y.: Learning to classify short text from scientific documents using topic models with various types of knowledge. Expert Syst. Appl. 42(3), 1684–1698 (2015)CrossRef
6.
Hu, X., Zhang, X., Lu, C., et al.: Exploiting Wikipedia as external knowledge for document clustering. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, pp. 389–396 (2009)
7.
Hu, J., Fang, L., Cao, Y.: Enhancing text clustering by leveraging Wikipedia semantics. In: Proceedings of the 31th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, pp. 179–186 (2008)
8.
Song, S., Zhu, H., Chen, L.: Probabilistic correlation-based similarity measure on text records. Inf. Sci. 289(1), 8–24 (2014)CrossRef
9.
Yang, L.L., Li, C.P., Ding, Q., et al.: Combining lexical and semantic features for short text classification. In: Proceedings of the 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES, pp. 78–86 (2013)
10.
Cheng, H., Qin, Z., Qian, W., et al.: Conditional mutual information based feature selection. In: International Symposium on Knowledge Acquisition and Modeling, pp. 103–107 (2008)
11.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
12.
Phan, X.H., Nguyen, L.M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proceedings of the 17th International Conference on World Wide Web, pp. 91–100. ACM, New York (2008)
13.
Chen, M., Jin, X., Shen, D.: Short text classification improved by learning multi-granularity topics. In: Proceedings of the 22th International Joint Conference on Artificial Intelligence, pp. 1776–1781 (2011)
14.
Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)CrossRef
15.
16.
Metadata
Title
Effectively Classifying Short Texts via Improved Lexical Category and Semantic Features
Authors
Huifang Ma
Runan Zhou
Fang Liu
Xiaoyong Lu
Copyright Year
2016
Book
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-42290-9

Electronic ISBN: 978-3-319-42291-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-42291-6_16

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