Supervised word sense disambiguation using new features based on word embeddings

Supervised Word Sense Disambiguation (WSD) systems use features of the target word and its context to learn about all possible samples in an annotated dataset. Recently, word embeddings have emerged as a powerful feature in many NLP tasks. In supervised WSD, word embeddings can be used as a high-quality feature representing the context of an ambiguous word. In this paper, four improvements to existing state-of-the-art WSD methods are proposed. First, we propose a new model for assigning vector coefficients for a more precise context representation. Second, we apply a PCA dimensionality reduction process to find a better transformation of feature matrices and train a more informative model. Third, a new weighting scheme is suggested to tackle the problem of unbalanced data in standard WSD datasets and finally, a novel idea is presented to combine word embedding features extracted from different independent corpora, which uses a voting aggregator among available trained models. All of these proposals individually improve disambiguation performance on Standard English lexical sample tasks, and using the combination of all proposed ideas makes a significant improvement in the accuracy score.