Locality-Convolution Kernel and Its Application to Dependency Parse Ranking

  • Evgeni Tsivtsivadze
  • Tapio Pahikkala
  • Jorma Boberg
  • Tapio Salakoski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


We propose a Locality-Convolution (LC) kernel in application to dependency parse ranking. The LC kernel measures parse similarities locally, within a small window constructed around each matching feature. Inside the window it makes use of a position sensitive function to take into account the order of the feature appearance. The similarity between two windows is calculated by computing the product of their common attributes and the kernel value is the sum of the window similarities. We applied the introduced kernel together with Regularized Least-Squares (RLS) algorithm to a dataset containing dependency parses obtained from a manually annotated biomedical corpus of 1100 sentences. Our experiments show that RLS with LC kernel performs better than the baseline method. The results outline the importance of local correlations and the order of feature appearance within the parse. Final validation demonstrates statistically significant increase in parse ranking performance.


Reproduce Kernel Hilbert Space Ranking Performance Convolution Kernel Local Correlation Graph Kernel 
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 2006

Authors and Affiliations

  • Evgeni Tsivtsivadze
    • 1
  • Tapio Pahikkala
    • 1
  • Jorma Boberg
    • 1
  • Tapio Salakoski
    • 1
  1. 1.Turku Centre for Computer Science (TUCS), Department of Information TechnologyUniversity of TurkuTurkuFinland

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