Learning dynamic word embeddings with drift regularisation
Computation and Language
2019-07-23 v1 Machine Learning
Abstract
Word usage, meaning and connotation change throughout time. Diachronic word embeddings are used to grasp these changes in an unsupervised way. In this paper, we use variants of the Dynamic Bernoulli Embeddings model to learn dynamic word embeddings, in order to identify notable properties of the model. The comparison is made on the New York Times Annotated Corpus in English and a set of articles from the French newspaper Le Monde covering the same period. This allows us to define a pipeline to analyse the evolution of words use across two languages.
Cite
@article{arxiv.1907.09169,
title = {Learning dynamic word embeddings with drift regularisation},
author = {Syrielle Montariol and Alexandre Allauzen},
journal= {arXiv preprint arXiv:1907.09169},
year = {2019}
}
Comments
Published at TALN 2019. in French