Keyqueries for Clustering and Labeling

  • Tim Gollub
  • Matthias Busse
  • Benno Stein
  • Matthias HagenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9994)


In this paper we revisit the document clustering problem from an information retrieval perspective. The idea is to use queries as features in the clustering process that finally also serve as descriptive cluster labels “for free.” Our novel perspective includes query constraints for clustering and cluster labeling that ensure consistency with a keyword-based reference search engine.

Our approach combines different methods in a three-step pipeline. Overall, a query-constrained variant of k-means using noun phrase queries against an ESA-based search engine performs best. In the evaluation, we introduce a soft clustering measure as well as a freely available extended version of the Ambient dataset. We compare our approach to two often-used baselines, descriptive k-means and k-means plus \(\chi ^2\). While the derived clusters are of comparable high quality, the evaluation of the corresponding cluster labels reveals a great diversity in the explanatory power. In a user study with 49 participants, the labels generated by our approach are of significantly higher discriminative power, leading to an increased human separability of the computed clusters.


Noun Phrase Search Query Retrieval Model Vector Space Model Head Noun 
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 International Publishing AG 2016

Authors and Affiliations

  • Tim Gollub
    • 1
  • Matthias Busse
    • 1
  • Benno Stein
    • 1
  • Matthias Hagen
    • 1
    Email author
  1. 1.Bauhaus-Universität WeimarWeimarGermany

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