Discriminative slot detection using kernel methods

Shubin Zhao, Adam Meyers, Ralph Grishman

Research output: Contribution to conferencePaperpeer-review


Most traditional information extraction approaches are generative models that assume events exist in text in certain patterns and these patterns can be regenerated in various ways. These assumptions limited the syntactic clues being considered for finding an event and confined these approaches to a particular syntactic level. This paper presents a discriminative framework based on kernel SVMs that takes into account different levels of syntactic information and automatically identifies the appropriate clues. Kernels are used to represent certain levels of syntactic structure and can be combined in principled ways as input for an SVM. We will show that by combining a low level sequence kernel with a high level kernel on a GLARF dependency graph, the new approach outperformed a good rule-based system on slot filler detection for MUC-6.

Original languageEnglish (US)
StatePublished - 2004
Event20th International Conference on Computational Linguistics, COLING 2004 - Geneva, Switzerland
Duration: Aug 23 2004Aug 27 2004


Conference20th International Conference on Computational Linguistics, COLING 2004

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Language and Linguistics
  • Linguistics and Language


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