Boosting automatic lexical acquisition with morphological information

  • Authors:
  • Massimiliano Ciaramita

  • Affiliations:
  • Brown University, Providence, RI

  • Venue:
  • ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper we investigate the impact of morphological features on the task of automatically extending a dictionary. We approach the problem as a pattern classification task and compare the performance of several models in classifying nouns that are unknown to a broad coverage dictionary. We used a boosting classifier to compare the performance of models that use different sets of features. We show how adding simple morphological features to a model greatly improves the classification performance.