Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling

Abstract

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.