Self Organizing Maps in NLP: Exploration of Coreference Feature Space

  • Andre Burkovski
  • Wiltrud Kessler
  • Gunther Heidemann
  • Hamidreza Kobdani
  • Hinrich Schütze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6731)


In Natural Language Processing, large annotated data sets are needed to train language models using supervised machine learning methods. To obtain such labeled data sets, time consuming manual annotation is required. To facilitate this process, we propose a SOM-based approach: The SOM sorts the data through unsupervised training, mapping the space of linguistic features to a 2D-grid. The grid visualization is used for efficient interactive labeling of the data clusters. In addition, the interactive SOM visualization allows computational linguists to explore the topology of the feature space and design new features.


self organizing maps coreference resolution annotation visualization natural language processing feature engineering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andre Burkovski
    • 1
  • Wiltrud Kessler
    • 1
  • Gunther Heidemann
    • 1
  • Hamidreza Kobdani
    • 2
  • Hinrich Schütze
    • 2
  1. 1.Intelligent Systems GroupUniversity of StuttgartStuttgartGermany
  2. 2.Institute for Natural Language ProcessingUniversity of StuttgartStuttgartGermany

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