This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints -that words tend to have one sense per discourse and one sense per collocation -exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96%.
REF proposes a method for word sense disambiguation, which is based on Monolingual Bootstrapping.
Unsupervised Word Sense Disambiguation Rivaling Supervised Methods
Annual Meeting Of The Association For Computational Linguistics
Computer Science