Word Sense Disambiguation Using OntoNotes: An Empirical Study

The accuracy of current word sense disambiguation (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 evaluation 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 technique using feature augmentation with active learning. Our results show that this approach is effective in reducing the annotation effort required to adapt a WSD system to a new domain. Finally, we propose that one can maximize the dual benefits of reducing the annotation effort while ensuring an increase in WSD accuracy, by only perfor...

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Zhi Zhong, Hwee Tou Ng, Yee Seng Chan
• Keywords: EMNLP 2008, Large-scale Wsd Evaluation, Natural Language Processing, Sense Inventory, WSD Systems