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The Effect of Bias on an Automatically-built Word Sense Corpus
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC 2004)
Abstract
The goal of this paper is to explore the large-scale automatic acquisition of sense-tagged examples to be used for Word Sense Disambiguation (WSD). We have applied the ``monosemous relatives'' method on the Web in order to build such a resource for all nouns in WordNet. The analysis of some parameters revealed that the distribution of the word senses (bias) in the training and test corpus is a determinant factor. Provided there is a method to approximate the bias for each word sense, the results we obtained for English are comparable to the use of hand-tagged data (Semcor), which is a very interesting perspective for lesser studied languages.