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

2000/05/07 by Ted Pedersen, Pedersen, Ted · 4 citations
Computer Science · #Bayesian Modeling and Causal Inference #Natural Language Processing Techniques #Topic Modeling #cs.CL

paper · pdf · doi:10.48550/arxiv.cs/0005006

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

arxiv created 2000/05/07 · arxiv updated 2009/11/30

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.

Cited by

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

Related

• A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
• Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited
• Word Sense Disambiguation using Optimised Combinations of Knowledge Sources
• Word-Sense Disambiguation Using Decomposable Models
• A Decision Tree of Bigrams is an Accurate Predictor of Word Sense