SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
This work provides an incremental improvement for researchers working on lexical semantic change detection by exploring the impact of alignment landmarks.
This paper presents SChME, an ensemble method for unsupervised lexical semantic change detection, which combines distributional and word frequency models. The method achieved competitive results in SemEval-2020 Task 1, demonstrating that the number of landmarks used for alignment directly impacts predictive performance.
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.