Time-Out : Temporal Referencing for Robust Modeling of Lexical Semantic Change

Code produced for this paper is available at: https://github.com/Garrafao/TemporalReferencingState-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.Peer reviewe

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

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.