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Locality-Convolution Kernel and Its Application to Dependency Parse Ranking

Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Locality-Convolution Kernel and Its Application to Dependency Parse Ranking. In: Advances in Applied Artificial Intelligence, 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Annecy, France, June 27-30, 2006. Proceedings, Lecture Notes in Computer Science 4031, 610–618, Springer, 2006.

Abstract:

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.

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Abstract in PDF-format

BibTeX entry:

@INPROCEEDINGS{inpTsPaBoSa06a,
  title = {Locality-Convolution Kernel and Its Application to Dependency Parse Ranking},
  booktitle = {Advances in Applied Artificial Intelligence, 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Annecy, France, June 27-30, 2006. Proceedings},
  author = {Tsivtsivadze, Evgeni and Pahikkala, Tapio and Boberg, Jorma and Salakoski, Tapio},
  volume = {4031},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  pages = {610–618},
  year = {2006},
  keywords = {kernel function, locality, convolution},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group

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