Paper: Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning

ACL ID P05-1049
Title Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

Shortage of manually sense-tagged data is an obstacle to supervised word sense dis- ambiguation methods. In this paper we in- vestigate a label propagation based semi- supervised learning algorithm for WSD, which combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: simi- lar examples should have similar labels. Our experimental results on benchmark corpora indicate that it consistently out- performs SVM when only very few la- beled examples are available, and its per- formance is also better than monolingual bootstrapping, and comparable to bilin- gual bootstrapping.