Word sense learning based on feature selection and MDL principle

In this paper, we propose a word sense learning algorithm which is capable of unsupervised feature selection and cluster number identification. Feature selection for word sense learning is built on an entropy-based filter and formalized as a constraint optimization problem, the output of which is a set of important features. Cluster number identification is built on a Gaussian mixture model with a MDL-based criterion, and the optimal model order is inferred by minimizing the criterion. To evaluate closeness between the learned sense clusters with the ground-truth classes, we introduce a kind of weighted F-measure to model the effort needed to reconstruct the classes from the clusters. Experiments show that the algorithm can retrieve important features, roughly estimate the class numbers automatically and outperforms other algorithms in terms of the weighted F-measure. In addition, we also try to apply the algorithm to a specific task of adding new words into a Chinese thesaurus.