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

Maurício Gruppi, Sibel Adali, Pin-Yu Chen


Abstract
This paper describes SChME (Semantic Change Detection with Model Ensemble), a method used in SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME uses a model ensemble combining signals distributional models (word embeddings) and word frequency where each model casts a vote indicating the probability that a word suffered semantic change according to that feature. More specifically, we combine cosine distance of word vectors 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 this process. Our results show evidence that the number of landmarks used for alignment has a direct impact on the predictive performance of the model. Moreover, we show that languages that suffer less semantic change tend to benefit from using a large number of landmarks, whereas languages with more semantic change benefit from a more careful choice of landmark number for alignment.
Anthology ID:
2020.semeval-1.11
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
105–111
Language:
URL:
https://aclanthology.org/2020.semeval-1.11
DOI:
10.18653/v1/2020.semeval-1.11
Bibkey:
Cite (ACL):
Maurício Gruppi, Sibel Adali, and Pin-Yu Chen. 2020. SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 105–111, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change (Gruppi et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.11.pdf
Code
 mgruppi/schme