Paper: Word Sense Disambiguation Using OntoNotes: An Empirical Study

ACL ID D08-1105
Title Word Sense Disambiguation Using OntoNotes: An Empirical Study
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008

The accuracy of current word sense disam- biguation (WSD) systems is affected by the fine-grained sense inventory of WordNet as well as a lack of training examples. Using the WSD examples provided through OntoNotes, we conduct the first large-scale WSD evalua- tion involving hundreds of word types and tens of thousands of sense-tagged examples, while adopting a coarse-grained sense inventory. We show that though WSD systems trained with a large number of examples can obtain a high level of accuracy, they nevertheless suffer a substantial drop in accuracy when applied to a different domain. To address this issue, we propose combining a domain adaptation tech- nique using feature augmentation with active learning. Our results show that this approach is effective in reducing the annotation ef...