Discriminative Slot Detection Using Kernel Methods

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA460647

Entities

People

  • Adam Meyers
  • Ralph David Grishman
  • Shubin Zhao

Organizations

  • New York University

Tags

Communities of Interest

  • Counter IED

DTIC Thesaurus Topics

  • Accuracy
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Computer Science
  • Detection
  • Information Processing
  • Information Systems
  • Kernel Functions
  • Language
  • Linguistics
  • Machine Learning
  • Neural Networks
  • New York
  • Precision
  • Rule Based Systems
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Machine Translation