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Estimating Keyphrases Popularity in Sampling Collections

  • Svetlana Popova
  • Gabriella Skitalinskaya
  • Ivan Khodyrev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9416)

Abstract

The problem of structured representation of data has high practical value and is particularly relevant due to growth of data volume. Such means of data representation as topic graphs, concepts trees, etc. is a convenient way to represent information retrieved from a collection of documents. In this paper, we research some aspects of using collection of samples for evaluation popularity of concepts. The last can be used to visualize concept significance and concept ranking in the tasks of structured representation.

Multi-word phrases are considered concepts. We address the case when these phrases are automatically extracted from the processed document collection. The popularity of a concept (e.g., visually can be presented as the size of the vertex in the topic graph) is judged by the number of documents containing this phrase. We elaborate the case when a sample from the document collection is used to estimate concept popularity. For this case we estimate how permissible is such representation of data, reflecting the proportions of the number of documents containing specific concepts. A frequency-based criterion and a procedure of its calculation is described in the paper. This helps to estimate the expedience of concept popularity representation in respect to the popularity of other concepts. The main aspect here is to establish criteria when relations between values of concepts popularity in a sample are the same as in the population, and establish criterion for selecting n high-frequency concepts which have the same sample rank and frequency distributions as in the population.

Keywords

Key phrase Topic graph Search result Information extraction Short texts Sampling 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Svetlana Popova
    • 1
    • 2
  • Gabriella Skitalinskaya
    • 3
  • Ivan Khodyrev
    • 1
  1. 1.ITMO UniversitySaint-PetersburgRussia
  2. 2.Saint-Petersburg State UniversitySaint-PetersburgRussia
  3. 3.Russian Presidential Academy of National Economy and PublicMoscowRussia

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