A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation

Abstract

This paper presents a corpus-based approach to word sense disambiguation that builds an ensemble of Naive Bayesian classifiers, each of which is based on lexical features that represent co--occurring words in varying sized windows of context. Despite the simplicity of this approach, empirical results disambiguating the widely studied nouns line and interest show that such an ensemble achieves accuracy rivaling the best previously published results.

7 pages, Latex, uses colnaacl.sty. Appears in Proceedings of NAACL, pages 63-69, May 2000, Seattle, WA

References in corpus (3)

• Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
• Exploiting Diversity in Natural Language Processing: Combining Parsers
• Sequential Model Selection for Word Sense Disambiguation

Cited by in corpus (5)

• Machine Learning with Lexical Features: The Duluth Approach to Senseval-2
• Evaluating the Effectiveness of Ensembles of Decision Trees in Disambiguating Senseval Lexical Samples
• Supervised and Unsupervised Ensembling for Knowledge Base Population
• Context based Analysis of Lexical Semantics for Hindi Language
• A Decision Tree of Bigrams is an Accurate Predictor of Word Sense