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AST Method for Scoring String-to-text Similarity

In: Clusters, Orders, and Trees: Methods and Applications

Author

Listed:
  • Ekaterina Chernyak

    (National Research University – Higher School of Economics)

  • Boris Mirkin

    (National Research University – Higher School of Economics)

Abstract

A suffix-tree-based method for measuring similarity of a key phrase to an unstructured text is proposed. The measure involves less computation and it does not depend on the length of the text or the key phrase. This applies to: 1. finding interrelations between key phrases over a set of texts; 2. annotating a research article by topics from a taxonomy of the domain; 3. clustering relevant topics and mapping clusters on a domain taxonomy.

Suggested Citation

  • Ekaterina Chernyak & Boris Mirkin, 2014. "AST Method for Scoring String-to-text Similarity," Springer Optimization and Its Applications, in: Fuad Aleskerov & Boris Goldengorin & Panos M. Pardalos (ed.), Clusters, Orders, and Trees: Methods and Applications, edition 127, pages 331-340, Springer.
    • Handle: RePEc:spr:spochp:978-1-4939-0742-7_20
    DOI: 10.1007/978-1-4939-0742-7_20
    as

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