Multi-task text segmentation and alignment based on weighted mutual information

ABSTRACT

Text segmentation is important for text analysis, while text alignment is to determine shared sub-topics among similar documents. Multi-task text segmentation and alignment is the extension of single-task segmentation to utilize information of multi-source documents. In this paper we introduce a novel domain-independent unsupervised method for multi-task segmentation and alignment based on the idea that the optimal segmentation and alignment maximizes weighted mutual information, mutual information with term weights. The experiment results show that our approach works well.

REFERENCES

• 1: Ron Bekkerman , Ran El-Yaniv , Andrew McCallum, Multi-way distributional clustering via pairwise interactions, Proceedings of the 22nd international conference on Machine learning, p.41-48, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102357]
• 2: Rich Caruana, Multitask Learning, Machine Learning, v.28 n.1, p.41-75, July 1997 [doi>10.1023/A:1007379606734]
• 3: Freddy Y. Y. Choi, Advances in domain independent linear text segmentation, Proceedings of the first conference on North American chapter of the Association for Computational Linguistics, p.26-33, April 29-May 04, 2000, Seattle, Washington
• 4: S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Systems, 1990.
• 5: Inderjit S. Dhillon , Subramanyam Mallela , Dharmendra S. Modha, Information-theoretic co-clustering, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956764]
• 6: T. Hofmann. Probabilistic latent semantic analysis. In Proc. UAI, 1999.
• 7: Xiang Ji , Hongyuan Zha, Domain-independent text segmentation using anisotropic diffusion and dynamic programming, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860494]
• 8: M. Utiyama and H. Isahara. A statistical model for domain-independent text segmentation. In Proc. ACL, pages 491--498, 1999.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms, Design, Experimentation

Keywords:
multi-task, text alignment, text segmentation, weighted mutual information