A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation
A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation
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发表时间：
2000-04
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通讯作者：
Ted Pedersen
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作者：
Ted Pedersen
本文提出了一种基于语料库的词义消歧方法，该方法建立了一个朴素贝叶斯分类器的集合，每个分类器都是基于词汇特征的，这些词汇特征代表了不同大小的上下文窗口中的同现词。尽管这种方法很简单，但消除广泛研究的名词行和兴趣的实证结果表明，这样的合奏达到了与以前发表的最佳结果相媲美的准确性。
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.