Graph-based Semi-Supervised Learning Algorithms for NLP
Amar Subramanya, Partha Talukdar · 2012
While labeled data is expensive to prepare, ever increasing amounts of unlabeled linguistic data are becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-ofthe-art in many applications in speech processing, computer vision and NLP. In particular, recent NLP research has successfully used graph-based SSL algorithms