Research in the field of WSD has been conducted in computational linguistics as a specific task for many years. Language and context features have been shown to be very helpful for the task of word sense disambiguation.
In this paper, we investigate the effectiveness of the graph-based ranking method on features from limited language data of word sense disambiguation. Contrary to existing method, we adopt different features and the result shows graph-based re-ranking approach improves the effective of word sense disambiguation systems.