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

Bingjun Sun, Ding Zhou, Hongyuan Zha, John Yen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006
Pages846-847
Number of pages2
DOIs
Publication statusPublished - Dec 1 2006
Event15th ACM Conference on Information and Knowledge Management, CIKM 2006 - Arlington, VA, United States
Duration: Nov 6 2006Nov 11 2006

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other15th ACM Conference on Information and Knowledge Management, CIKM 2006
CountryUnited States
CityArlington, VA
Period11/6/0611/11/06

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All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Cite this

Sun, B., Zhou, D., Zha, H., & Yen, J. (2006). Multi-task text segmentation and alignment based on weighted mutual information. In Proceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006 (pp. 846-847). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/1183614.1183760