Graph-Based, Supervised Machine Learning Approach to (Irregular) Polysemy in WordNet

This paper presents a supervised machine learning approach that aims at annotating those homograph word forms in WordNet that share some common meaning and can hence be thought of as belonging to a polysemous word. Using different graph-based measures, a set of features is selected, and a random forest model is trained and evaluated. The results are compared to other features used for polysemy identification in WordNet. The features proposed in this paper not only outperform the commonly used CoreLex resource, but they also work on different parts of speech and can be used to identify both regular and irregular polysemous word forms in WordNet.