Named Entity Recognition as a House of Cards: Classifier Stacking

Abstract

This paper presents a classifier stacking-based approach to the named entity recognition task (NER henceforth). Transformation-based learning (Brill, 1995), Snow (sparse network of winnows (Mu oz et al., 1999)) and a forward-backward algorithm are stacked (the output of one classifier is passed as input to the next classifier), yielding considerable improvement in performance. In addition, in agreement with other studies on the same problem, the enhancement of the feature space (in the form of capitalization information) is shown to be especially beneficial to this task.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA459582

Entities

People

  • Radu Florian

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Applied Computer Science
  • Artificial Intelligence
  • Computational Linguistics
  • Computer Science
  • Errors
  • Identification
  • Iterations
  • Language
  • Learning
  • Linguistics
  • Machine Learning
  • Named Entity Recognition
  • Natural Language Processing
  • Precision
  • Recognition

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Explosive Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks
  • Space