Learning Rules for Large-Vocabulary Word Sense Disambiguation: A Comparison of Various Classifiers

In this article we compare the performance of various machine learning algorithms on the task of constructing word-sense disambiguation rules from data. The distinguishing characteristic of our work from most of the related work in the field is that we aim at the disambiguation of all content words in the text, rather than focussing on a small number of words. In an earlier study we have shown that a decision tree induction algorithm performs well on this task. This study compares decision tree induction with other popular learning methods and discusses their advantages and disadvantages. Our results confirm the good performance of decision tree induction, which outperforms the other algorithms, due to its ability to order the features used for disambiguation, according to their contribution in assigning the correct sense.

• Type: Conference
• Year: 2000
• Where: NLP
• Authors: Georgios Paliouras, Vangelis Karkaletsis, Ion Androutsopoulos, Constantine D. Spyropoulos
• Keywords: Decision Tree Induction, Disambiguation, Natural Language Processing, NLP 2000, Word-sense Disambiguation Rules