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A Latent Semantic Indexing-based approach to multilingual document clastering

This paper explores a Latent Semantic Indexing (LSI)-based method for clustering multilingual documents. We detail the LSI technique, which employs singular value decomposition to reduce dimensionality while preserving meaningful relationships between terms and documents. This study includes an empirical evaluation of the technique’s effectiveness in clustering multi-language documents, analyzing factors like synonymy, polysemy, and vocabulary differences across languages. The results demonstrate the efficacy of LSI in creating a multilingual semantic space, ultimately enhancing decision support systems.

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A Latent Semantic Indexing-based approach to multilingual document clastering

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  1. A Latent Semantic Indexing-based approach to multilingual document clastering Chih-Ping Wei, Christopher C. Yang, Chia-Min Lin Decision Support Systems 45 (2008) 606-620 Reporter : Yi Ru, Lee

  2. Outline Introduction Latent Semantic Indexing(LSI) LSI-based multilingual document clustering technique Empirical evaluation Conclusion

  3. Introduction • Translation-based • Synonymy • Polysemy • vocabulary • Multilingual space • Latent Semantic Indexing(LSI) • Lexical matching • Reduce the dimensions

  4. Latent Semantic Indexing(con.) Singular Value Decomposition (SVD)

  5. Latent Semantic Indexing(con.)

  6. LSI-based multilingual document clustering technique(con.)

  7. LSI-based multilingual document clustering technique(con.) Multilingual semantic space analysis

  8. LSI-based multilingual document clustering technique(con.) Document folding-in

  9. LSI-based multilingual document clustering technique(con.) Dj denote the LSI dimension j Wji is the weight of document i in Dj Dimension Selection

  10. LSI-based multilingual document clustering technique(con.) • Clustering • Hierarchical clustering algorithm

  11. Empirical evaluation(con.)

  12. Empirical evaluation(con.) TA is the set of associations in the true categories. GA is the set of associations in the clusters generated by the document clustering technique. CA is the set of correct associations that exists in both the clusters and the true categories.

  13. Empirical evaluation(con.) TA={(e1−e2),(c1−c2), (e1−c1), (e1−c2), (e2−c1), (e2−c2), (e3−e4),(c3−c4), (c3−c5), (c4−c5), (e3−c3), (e3−c4), (e3−c5), (e4−c3), (e4−c4), (e4−c5)} GA={(e1−e2), (c1−c3), (e1−c1), (e1−c3), (e2−c1), (e2−c3), (e3−e4), (e3−c2), (e4−c2), (c4−c5)} CA={(e1−e2), (e1−c1), (e2−c1), (e3−e4), (c4−c5)} Examples

  14. Empirical evaluation(con.) PRT curves of the LSI-based MLDC technique

  15. Empirical evaluation(con.) Comparisons of different representation schemes

  16. Empirical evaluation(con.) Effect of dimension selection (h=5 for MLDC with dimension selection; k=5 for MLDC without dimension selection)

  17. Empirical evaluation(con.) Effect of dimension selection (h=20 for MLDC with dimension selection; k=20 for MLDC without dimension selection)

  18. Empirical evaluation(con.) Best scenario versus best scenario comparison

  19. Empirical evaluation(con.) PRT curves of overall, monolingual, and cross-lingual performance

  20. Conclusion monolingual PRT curve > overall PRT curve > cross-lingual PRT curve Specific domain

  21. Thank you

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