As archives contain documents that span over a long period of time, the language used to create these documents and the language used for querying the archive can differ. This difference is due to evolution in both terminology and semantics and will cause a significant number of relevant documents being omitted. A static solution is to use query expansion based on explicit knowledge banks such as thesauri or ontologies. However as we are able to archive resources with more varied terminology, it will be infeasible to use only explicit knowledge for this purpose. There exist only few or no thesauri covering very domain specific terminologies or slang as used in blogs etc. In this Ph.D. thesis we focus on automatically detecting terminology evolution in a completely unsupervised manner as described in this technical paper.


Automatic Detection Query Expansion Word Sense Computational Linguistics Cluster Evolution 
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 2009

Authors and Affiliations

  • Nina Tahmasebi
    • 1
  1. 1.L3S Research CenterHannoverGermany

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