A Domain Independent Semantic Measure for Keyword Sense Disambiguation

María G. Buey, Carlos Bobed, Jorge Gracia, Eduardo Mena
2021 Zenodo  
Understanding the user's intention is crucial for many tasks that involve human-machine interaction. To that end, word sense disambiguation (WSD) techniques play an important role. WSD techniques typically require well-formed sentences as context to operate, as well as pre-defined catalogues of word senses. However, there are some scenarios on the Web where such conditions do not apply well, such as when there is a need to disambiguate keywords from a query, or sets of tags describing any Web
more » ... source, where the context does not come as well-formed sentences. In this paper, we propose an approach to disambiguate sets of keywords by linking them to concepts of a given ontology that is not known at training time. Our approach grounds on a semantic relatedness measure between words and concepts, and explores different disambiguation algorithms to study the contribution of both word and sentence-level representations. We focus on situations where the available linguistic information is very scarce (e.g., keyword-based / Web search queries), hampering natural language based approaches. Experimental results show the feasibility of our approach in general and in specific knowledge domains without previous training for the target domain.
doi:10.5281/zenodo.4631684 fatcat:vdu3jzuzonfvzejc4dwewepkwy