An Experimental Study on Unsupervised Graph-based Word Sense Disambiguation

Recent research works on unsupervised word sense disambiguation report an increase in performance, which reduces their handicap from the respective supervised approaches for the same task. Among the latest state of the art methods, those that use semantic graphs reported the best results. Such methods create a graph comprising the words to be disambiguated and their corresponding candidate senses. The graph is expanded by adding semantic edges and nodes from a thesaurus. The selection of the most appropriate sense per word occurrence is then made through the use of graph processing algorithms that offer a degree of importance among the graph vertices. In this paper we experimentally investigate the performance of such methods. We additionally evaluate a new method, which is based on a recently introduced algorithm for computing similarity between graph vertices, P-Rank. We evaluate the performance of all alternatives in two benchmark data sets. Senseval 2 and 3, using WordNet. The current study shows the differences in the performance of each method, when applied on the same semantic graph representation, and analyzes the pros and cons of each method for each part of speech separately. Furthermore, it analyzes the levels of inter-agreement in the sense selection level, giving further insight on how these methods could be employed in an unsupervised ensemble for word sense disambiguation.