Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity

In this paper, we discuss the importance of the quality against the quantity of automatically extracted examples for word sense disambiguation (WSD). We first show that we can build a competitive WSD system with a memory-based classifier and a feature set reduced to easily and eciently computable features. We then show that adding automatically annotated examples improves the performance of this system when the examples are carefully selected based on their quality.

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