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AbstractState-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, …...

AbstractWe present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has …...

AbstractThis paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing, learning …...