This paper introduces a graph-based algorithm for sequence data labeling, using random walks on graphs encoding label dependencies. The algorithm is illustrated and tested in the context of an unsupervised word sense disambiguation problem, and shown to significantly outperform the accuracy achieved through individual label assignment, as measured on standard senseannotated data sets.
REF introduced a graph based unsupervised technique for all word sense disambiguation.
Unsupervised Large-Vocabulary Word Sense Disambiguation With Graph-Based Algorithms For Sequence Data Labeling
Human Language Technology Conference And Empirical Methods In Natural Language Processing
Computer Science