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This paper describes SChME (Semantic Change Detection with Model Ensemble), amethod usedin SemEval-2020 Task 1 on unsupervised detection of lexical semanticchange. SChME usesa model ensemble combining signals of distributional models(word embeddings) and wordfrequency models where each model casts a voteindicating the probability that a word sufferedsemantic change according tothat feature. More specifically, we combine cosine distance of wordvectorscombined with a neighborhood-based metric we named Mapped NeighborhoodDistance(MAP), and a word frequency differential metric as input signals to ourmodel. Additionally,we explore alignment-based methods to investigate theimportance of the landmarks used in thisprocess. Our results show evidence thatthe number of landmarks used for alignment has a directimpact on the predictiveperformance of the model. Moreover, we show that languages that sufferlesssemantic change tend to benefit from using a large number of landmarks, whereaslanguageswith more semantic change benefit from a more careful choice oflandmark number for alignment.

SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change