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

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

URL: http://arxiv.org/abs/2012.01603v1
Date: Wed, 2 Dec 2020 23:56:34 GMT
Title: SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
Authors: Maur\'icio Gruppi, Sibel Adali and Pin-Yu Chen
Abstract summary: This paper describes SChME, a method used in 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.
Score: 58.87961226278285
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.