An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages

LREC 2018 Dmitry UstalovDenis TeslenkoAlexander PanchenkoMikhail ChernoskutovChris BiemannSimone Paolo Ponzetto

In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with respect to the semantic similarity between the given sentence and the synset constituting the sense of the target word... (read more)

PDF Abstract

Evaluation results from the paper


  Submit results from this paper to get state-of-the-art GitHub badges and help community compare results to other papers.