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A Dependency Graph Isomorphism for News Sentence Searching

  • Kim Schouten
  • Flavius Frasincar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)

Abstract

Given that the amount of news being published is only increasing, an effective search tool is invaluable to many Web-based companies. With word-based approaches ignoring much of the information in texts, we propose Destiny, a linguistic approach that leverages the syntactic information in sentences by representing sentences as graphs with disambiguated words as nodes and grammatical relations as edges. Destiny performs approximate sub-graph isomorphism on the query graph and the news sentence graphs, exploiting word synonymy as well as hypernymy. Employing a custom corpus of user-rated queries and sentences, the algorithm is evaluated using the normalized Discounted Cumulative Gain, Spearman’s Rho, and Mean Average Precision and it is shown that Destiny performs significantly better than a TF-IDF baseline on the considered measures and corpus.

Keywords

Mean Average Precision Word Sense Query Graph Grammatical Relation Discount Cumulative Gain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kim Schouten
    • 1
  • Flavius Frasincar
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

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