Paper: An Empirical Evaluation Of Knowledge Sources And Learning Algorithms For Word Sense Disambiguation

ACL ID W02-1006
Title An Empirical Evaluation Of Knowledge Sources And Learning Algorithms For Word Sense Disambiguation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2002
Authors

In this paper, we evaluate a vari- ety of knowledge sources and super- vised learning algorithms for word sense disambiguation on SENSEVAL-2 and SENSEVAL-1 data. Our knowledge sources include the part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic relations. The learning al- gorithms evaluated include Support Vec- tor Machines (SVM), Naive Bayes, Ad- aBoost, and decision tree algorithms. We present empirical results showing the rela- tive contribution of the component knowl- edge sources and the different learning algorithms. In particular, using all of these knowledge sources and SVM (i.e. , a single learning algorithm) achieves ac- curacy higher than the best official scores on both SENSEVAL-2 and SENSEVAL-1 test data.