combination of Semantic Relatedness with Supervised Method for Word Sense Disambiguation

We present a semi-supervised learning method at efficiently exploits semantic relatedness in order to incorporate sense knowledge into a word sense disambiguation model and to leverage system performance. We have presented sense relativeness algorithms which combine neural model learned from a generic embedding function for variable length contexts of target words on a POS-labeled text corpus, with sense-labeled data in the form of example sentences. This paper investigates the way of incorporating semantic relatedness in a word sense disambiguation setting and evaluates the method on some SensEval/SemEval lexical sample tasks. The obtained results show that such representations consistently improve the accuracy of the selective supervised WSD system.