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.
Mihalcea REF introduced an unsupervised method for disambiguating the senses of words using random walks on graphs that encode the dependencies between word senses.
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