Shortage of manually sense-tagged data is an obstacle to supervised word sense disambiguation methods. In this paper we investigate a label propagation based semisupervised learning algorithm for WSD, which combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar examples should have similar labels. Our experimental results on benchmark corpora indicate that it consistently outperforms SVM when only very few labeled examples are available, and its performance is also better than monolingual bootstrapping, and comparable to bilingual bootstrapping.
In REF , a label propagation algorithm was proposed for word sense disambiguation and compared to bootstrapping and a SVM supervised classifier.
Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning
Annual Meeting Of The Association For Computational Linguistics
Computer Science