Knowledge Graph Extension for Word Sense Annotation

One of the most successful approaches to Word Sense Disambiguation (WSD) in the last decade has been the knowledge-based approach, which exploits lexical knowledge sources such as wordnets, ontologies, etc. The knowledge encoded in them is typically used as a sense inventory and as a relations bank. However, this type of information is rather sparse in terms of senses and the relations among them. In this paper we present a strategy for the enrichment of WSD knowledge bases with data-driven relations from a gold standard corpus (annotated with word senses, valency information, syntactic analyses, etc.). We focus on Bulgarian and English as use cases, but our approach is scalable to other languages as well. The results show that the addition of new knowledge improves accuracy on the WSD task in a statistically significant way.