Correlating multilingual documents via bipartite graph modeling

ABSTRACT

There is enormous amount of multilingual documents from various sources and possibly from different countries describing a single event or a set of related events. It is desirable to construct text mining methods that can compare and highlight similarities and differences of those multilingual documents. We discuss our ongoing research that seeks to model a pair of multilingual documents as a weighted bipartite graph with the edge weights computed by means of machine translation. We use spectral method to identify dense subgraphs of the weighted bipartite graph which can be considered as corresponding to sentences that correlate well in textual contents. We illustrate our approach using English and German texts.

REFERENCES

• 1: Systran: Information and Translation Technologies. http://www.systransoft.com/
• 2: Translingual Information Detection, Extraction and Summarization. http://www.darpa.mil/ito/research/tides/
• 3: Hongyuan Zha , Xiaofeng He , Chris Ding , Horst Simon , Ming Gu, Bipartite graph partitioning and data clustering, Proceedings of the tenth international conference on Information and knowledge management, October 05-10, 2001, Atlanta, Georgia, USA [doi>10.1145/502585.502591]

INDEX TERMS

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

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.3 Numerical Linear Algebra
          Subjects: Singular value decomposition
  G.2 DISCRETE MATHEMATICS
      G.2.2 Graph Theory
          Subjects: Graph algorithms

  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.m Miscellaneous

General Terms:
Algorithms, Experimentation, Theory

Keywords:
bipartite graph, graph partitioning, singular value decomposition, translingual text summarization