Automatic acknowledgement indexing: expanding the semantics of contribution in the CiteSeer digital library

ABSTRACT

Acknowledgements in research publications, like citations, indicate influential contributions to scientific work; however, large-scale acknowledgement analyses have traditionally been impractical due to the high cost of manual information extraction. In this paper we describe a mixture method for automatically mining acknowledgements from research documents using a combination of a Support Vector Machine and regular expressions. The algorithm has been implemented as a plug-in to the CiteSeer Digital Library and the extraction results have been integrated with the traditional metadata and citation index of the CiteSeer system. As a demonstration, we use CiteSeer's autonomous citation indexing (ACI) feature to measure the relative impact of acknowledged entities, and present the top twenty acknowledged entities within the archive.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods; Linguistic processing
      H.3.7 Digital Libraries
          Subjects: Collection

General Terms:
Algorithms, Measurement

Keywords:
CiteSeer, acknowledgements, information extraction, text mining