A Topic Description Model Based on Two-Layer Kl Distance

Abstract:

The main challenge of Topic Detection and Tracking (TDT) for Blog is the insufficient information in a topic description and the lack of key words input by users. We propose a Two-layer KL Distance approach which combines the KL distance model with a lexical semantic association matrix model. First, the KL Distance model captured the weights of Initial feature words. Second, the KL Distance model was used again to estimate weights of words linked with initial feature words in the lexical Semantic Association Matrix. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of the two-layer of KL Distance.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 791-794
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.791
• Online since: July 2013
• Authors: Da Zhen Lin, Xian Ming Lin, Dong Lin Cao
• Keywords: Kl Distance, Topic Description

References

[1] Yang Y., J. Zhang, et al., Topic-conditioned novelty detection, in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining [C]. 2002, ACM: Edmonton, Alberta, Canada. pp.688-693.

DOI: 10.1145/775047.775150

[2] Brants T., F. Chen, et al., A System for new event detection, in Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval [C]. 2003, ACM: Toronto, Canada. pp.330-337.

DOI: 10.1145/860435.860495

[3] Changli Zhang, Research on Domain-Oriented Public Sentiment Analysis Technology [D]. Jilin University, 2011, PhD: 120.

[4] Seymore K., R. Rosenfeld. Large-scale Topic Detection and Language Model Adaptation [R]. (1997).

DOI: 10.21236/ada327553

[5] Hubert T. L., H. Jin, et al., The BBN Crosslingual Topic Detection and Tracking System, in In Working Notes of the Third Topic Detection and Tracking Workshop [C]. 2000. pp.894-01.

[6] Nallapati R., Semantic language models for topic detection and tracking, in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3 [C]. 2003, Association for Computational Linguistics: Edmonton, Canada. pp.1-6.

DOI: 10.3115/1073416.1073417

[7] Ruifang H., Q. Bing, et al., Topic Detection and Tracking with Topic Sensitive Language Model, in In International Conference on Mutilingual Information Processing [C]. 2005. pp.324-327.

[8] Lee C., G. G. Lee, et al. Dependency structure language model for topic detection and tracking [J]. Inf. Process. Manage., 2007, 43(5): 1249-1259.

DOI: 10.1016/j.ipm.2006.02.007

[9] Ha-Thuc V., P. Srinivasan, Topic models and a revisit of text-related applications, in Proceedings of the 2nd PhD workshop on Information and knowledge management [C]. 2008, ACM: Napa Valley, California, USA. pp.25-32.

DOI: 10.1145/1458550.1458556

[10] Lin J., R. Snow, et al., Smoothing techniques for adaptive online language models: topic tracking in tweet streams, in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining [C]. 2011, ACM: San Diego, California, USA. pp.422-429.

DOI: 10.1145/2020408.2020476