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

This paper describes SChME (Semantic Change Detection with Model Ensemble), a method usedin SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME usesa model ensemble combining signals of distributional models (word embeddings) and wordfrequency models where each model casts a vote indicating the probability that a word sufferedsemantic change according to that feature. More specifically, we combine cosine distance of wordvectors combined with a neighborhood-based metric we named Mapped Neighborhood Distance(MAP), and a word frequency differential metric as input signals to our model. Additionally,we explore alignment-based methods to investigate the importance of the landmarks used in thisprocess. Our results show evidence that the number of landmarks used for alignment has a directimpact on the predictive performance of the model. Moreover, we show that languages that sufferless semantic change tend to benefit from using a large number of landmarks, whereas languageswith more semantic change benefit from a more careful choice of landmark number for alignment.