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Graph Kernels versus Graph Representations: a Case Study in Parse Ranking

Tapio Pahikkala, Evgeni Tsivtsivadze, Jorma Boberg, Tapio Salakoski, Graph Kernels versus Graph Representations: a Case Study in Parse Ranking. In: Thomas Gärtner, Gemma C. Garriga, Thorsten Meinl (Eds.), Proceedings of the ECML/PKDD'06 workshop on Mining and Learning with Graphs (MLG'06), 181-188, 2006.

Abstract:

Recently, several kernel functions designed for a data that consists of graphs have been presented. In this paper, we concentrate on designing graph representations and adapting the kernels for these graphs. In particular, we propose graph representations for dependency parses and analyse the applicability of several variations of the graph kernels for the problem of parse ranking in the domain of biomedical texts. The parses used in the study are generated with the link grammar (LG) parser from annotated sentences of BioInfer corpus. The results indicate that designing the graph representation is as important as designing the kernel function that is used as the similarity measure of the graphs.

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BibTeX entry:

@INPROCEEDINGS{inpPaTsBoSa06a,
  title = {Graph Kernels versus Graph Representations: a Case Study in Parse Ranking},
  booktitle = {Proceedings of the ECML/PKDD'06 workshop on Mining and Learning with Graphs (MLG'06)},
  author = {Pahikkala, Tapio and Tsivtsivadze, Evgeni and Boberg, Jorma and Salakoski, Tapio},
  editor = {Gärtner, Thomas and Garriga, Gemma C. and Meinl, Thorsten},
  pages = {181-188},
  year = {2006},
}

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

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