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Using Noun Phrase Heads to Extract Document Keyphrases

  • Ken Barker
  • Nadia Cornacchia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1822)

Abstract

Automatically extracting keyphrases from documents is a task with many applications in information retrieval and natural language processing. Document retrieval can be biased towards documents containing relevant keyphrases; documents can be classified or categorized based on their keyphrases; automatic text summarization may extract sentences with high keyphrase scores.

This paper describes a simple system for choosing noun phrases from a document as keyphrases. A noun phrase is chosen based on its length, its frequency and the frequency of its head noun. Noun phrases are extracted from a text using a base noun phrase skimmer and an off-the-shelf online dictionary.

Experiments involving human judges reveal several interesting results: the simple noun phrase-based system performs roughly as well as a state-of-the-art, corpus-trained keyphrase extractor; ratings for individual keyphrases do not necessarily correlate with ratings for sets of keyphrases for a document; agreement among unbiased judges on the keyphrase rating task is poor.

Keywords

Noun Phrase Natural Language Processing Head Noun Candidate Phrase Human Judge 
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 2000

Authors and Affiliations

  • Ken Barker
    • 1
  • Nadia Cornacchia
    • 1
  1. 1.School of Information and Technology EngineeringUniversity of OttawaOttawaCanada

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