ELMo and BERT in semantic change detection for Russian
Computation and Language
2020-10-08 v1
Abstract
We study the effectiveness of contextualized embeddings for the task of diachronic semantic change detection for Russian language data. Evaluation test sets consist of Russian nouns and adjectives annotated based on their occurrences in texts created in pre-Soviet, Soviet and post-Soviet time periods. ELMo and BERT architectures are compared on the task of ranking Russian words according to the degree of their semantic change over time. We use several methods for aggregation of contextualized embeddings from these architectures and evaluate their performance. Finally, we compare unsupervised and supervised techniques in this task.
Keywords
Cite
@article{arxiv.2010.03481,
title = {ELMo and BERT in semantic change detection for Russian},
author = {Julia Rodina and Yuliya Trofimova and Andrey Kutuzov and Ekaterina Artemova},
journal= {arXiv preprint arXiv:2010.03481},
year = {2020}
}
Comments
The 9th International Conference on Analysis of Images, Social Networks and Texts (AIST 2020)