Improving Term Extraction with Terminological Resources

  • Sophie Aubin
  • Thierry Hamon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4139)


Studies of different term extractors on a corpus of the biomedical domain revealed decreasing performances when applied to highly technical texts. Facing the difficulty or impossibility to customize existing tools, we developed a tunable term extractor. It exploits linguistic-based rules in combination with the reuse of existing terminologies, i.e. exogenous disambiguation. Experiments reported here show that the combination of the two strategies allows the extraction of a greater number of term candidates with a higher level of reliability. We further describe the extraction process involving both endogenous and exogenous disambiguation implemented in the term extractor \(\rm Y\kern-.36em \lower.7ex\hbox{A}\kern-.25em T\kern-.1667em\lower.7ex\hbox{E}\kern-.08emA\).


Noun Phrase Term Candidate Term Extractor Content Coverage Nominal Phrase 
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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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sophie Aubin
    • 1
  • Thierry Hamon
    • 1
  1. 1.UMR CNRS 7030LIPNVilletaneuse

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