A Decision Tree of Bigrams is an Accurate Predictor of Word Sense
Ted Pedersen
TL;DR
This paper introduces a decision tree method utilizing bigram context for word sense disambiguation, demonstrating superior accuracy over previous approaches on SENSEVAL data.
Contribution
The paper presents a novel corpus-based decision tree approach for word sense disambiguation using bigram features, achieving higher accuracy than existing methods.
Findings
More accurate than average results for 30 of 36 words
Outperforms best previous results for 19 of 36 words
Effective use of bigram context in disambiguation
Abstract
This paper presents a corpus-based approach to word sense disambiguation where a decision tree assigns a sense to an ambiguous word based on the bigrams that occur nearby. This approach is evaluated using the sense-tagged corpora from the 1998 SENSEVAL word sense disambiguation exercise. It is more accurate than the average results reported for 30 of 36 words, and is more accurate than the best results for 19 of 36 words.
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Taxonomy
TopicsNatural Language Processing Techniques · Speech and dialogue systems · Video Analysis and Summarization