Paving the Way to a Large-scale Pseudosense-annotated Dataset

Mohammad Taher Pilehvar and Roberto Navigli

In this paper we propose a new approach to the generation of pseudowords, i.e., artificial words which model real polysemous words. Our approach simultaneously addresses the two important issues that hamper the generation of large pseudosense-annotated datasets: semantic awareness and coverage. We evaluate these pseudowords from three different perspectives showing that they can be used as reliable substitutes for their real counterparts.

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