In computational linguistics, word-sense induction (WSI) or discrimination is an open problem of natural language processing, which concerns the automatic identification of the senses of a word (i.e. meanings).
A well-known approach to context clustering is the Context-group Discrimination algorithm [4] based on large matrix computation methods.
[10] To deal with this issue several graph-based algorithms have been proposed, which are based on simple graph patterns, namely Curvature Clustering, Squares, Triangles and Diamonds (SquaT++), and Balanced Maximum Spanning Tree Clustering (B-MST).
[11] The patterns aim at identifying meanings using the local structural properties of the co-occurrence graph.
By applying co-occurrence graphs approaches have been shown to achieve the state-of-the-art performance in standard evaluation tasks.