Abstract
Clustering is widely used in topic detection task. However, the vector space model based distance, such as cosine-like distance, will get a low precision and recall when the corpus contains many related topics. In this paper, we propose a new distance measure method: the Topic Model (TM) induced distance. Assuming that the distribution of word is different in each topic, the documents can be treated as a sample of the mixture of k topic models, which can be estimated using expectation maximization (EM). A biased initiation method is proposed in this paper for topic decomposition using EM, which will generate a converged matrix for the generation of TM induced distance. The collections of web news are clustered into classes using this TM distance. A series of experiments are described on a corpus containing 5033 web news from 30 topics. K-means clustering is processed on test set with different topic numbers. A comparison of clustering result using the TM induced distance and the traditional cosine-like distance are given. The experiment results show that the proposed topic decomposition method using biased initiation is effective than the topic decomposition using random values. The TM induced distance will generate more topical groups than the VS model based cosine-like distance. In the web news collections containing related topics, the TM induced distance can achieve a better precision and recall.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: SIGIR 1998: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 37–45 (1998)
Chen, K.Y., Luesukprasert, L., Chou, S.T.: Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling. IEEE Trans. on Knowl. and Data Eng., 1016–1025 (2007)
Gildea, D., Hofmann, T.: Topic-Based Language models using EM. In: Proceedings of the 6th European Conference on Speech Communication and Technology, pp. 109–110 (1999)
Erkan, G., Radev, D.R.: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. J. Artif. Int. Res., 457–479 (2004)
Jain, A.K., Merty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv., 264–323 (1999)
Jiang, W., Guan, Y., Wang, X.: A Pragmatic Chinese Word Segmentation System. In: Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, Sydney, pp. 189–192 (2006)
Kelly, D., Díaz, F., Belkin, N.J., Allan, J.: A User-Centered Approach to Evaluating Topic Models. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 27–41. Springer, Heidelberg (2004)
Li, H., Yamanishi, K.: Topic analysis using a finite mixture model. In: Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora., pp. 35–44. ACM, NJ (2000)
Liu, S., Merhav, Y., Yee, W.G.: A sentence level probabilistic model for evolutionary theme pattern mining from news corpora. In: SAC 2009: Proceedings of the 2009 ACM symposium on Applied Computing, pp. 1742–1747. ACM, New York (2009)
Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: KDD 2005: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 198–207. ACM, New York (2005)
Michael, S., George, K., Vipin, K.: A Comparison of Document Clustering Techniques. In: KDD 2000, 6th ACM SIGKDD International Conference on Data Mining, Sydney, pp. 109–110 (2000)
Pons-Porrata, A., Berlanga-Llavori, R., Ruiz-Shulcloper, J.: Topic discovery based on text mining techniques. Inf. Process. Manage., 752–768 (2007)
Sun, B., Mitra, P., Giles, C.L., Yen, J., Zha, H.: Topic segmentation with shared topic detection and alignment of multiple documents. In: SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 199–206. ACM, New York (2007)
Zhai, C., Velielli, A., Yu, B.: A cross-collection mixture model for comparative text mining. In: KDD 2004: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 743–748. ACM, Seattle (2004)
Zobel, J., Moffat, A.: Exploring the similarity space. In: SIGIR Forum, pp. 18–34. ACM, New York (1998)
The 2004 Topic Detection and Tracking Task Definition and Evaluation Plan (2004), http://www.nist.gov/speech/tests/tdt/
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
Wu, Y., Ding, Y., Wang, X., Xu, J. (2010). Topic Detection by Topic Model Induced Distance Using Biased Initiation. In: Kim, Th., Adeli, H. (eds) Advances in Computer Science and Information Technology. AST ACN 2010 2010. Lecture Notes in Computer Science, vol 6059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13577-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-13577-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13576-7
Online ISBN: 978-3-642-13577-4
eBook Packages: Computer ScienceComputer Science (R0)