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

TL;DR

This paper presents SChME, an ensemble approach combining distributional and frequency models for detecting lexical semantic change, demonstrating the importance of landmark selection and alignment in multilingual settings.

Contribution

Introduces SChME, a novel ensemble method integrating multiple signals for unsupervised semantic change detection, and analyzes landmark effects on model performance across languages.

Findings

Landmark quantity impacts model accuracy.

Languages with less change benefit from more landmarks.

Languages with more change require careful landmark selection.

Abstract

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.