Can a Decision Tree Based on Bigrams Accurately Predict Word Sense?

Title: Can a Decision Tree Based on Bigrams Accurately Predict Word Sense?

Research Question: Can a decision tree based on bigrams accurately predict word sense?

Methodology: The researchers used a corpus-based approach to word sense disambiguation, where a decision tree assigned a sense to an ambiguous word based on the bigrams that occurred nearby. They evaluated their approach using sense-tagged corpora from the 1998 SENSEVAL word sense disambiguation exercise.

Results: The study found that the decision tree approach was more accurate than the average results reported for 30 of 36 words, and more accurate than the best results for 19 of 36 words.

Implications: These findings suggest that a decision tree based on bigrams can accurately predict word sense, providing an efficient and effective method for disambiguation in natural language processing.

Link to Article: https://arxiv.org/abs/0103026v1 Authors: arXiv ID: 0103026v1