Semantic Change Detection for the Romanian Language
Ciprian-Octavian Truică Victor Tudose Elena-Simona Apostol
Automatic semantic change methods try to identify the changes that appear
over time in the meaning of words by analyzing their usage in diachronic
corpora. In this paper, we analyze different strategies to create static and
contextual word embedding models, i.e., Word2Vec and ELMo, on real-world
English and Romanian datasets. To test our pipeline and determine the
performance of our models, we first evaluate both word embedding models on an
English dataset (SEMEVAL-CCOHA). Afterward, we focus our experiments on a
Romanian dataset, and we underline different aspects of semantic changes in
this low-resource language, such as meaning acquisition and loss. The
experimental results show that, depending on the corpus, the most important
factors to consider are the choice of model and the distance to calculate a
score for detecting semantic change.