Word Embeddings for Romanian Language and Their Use for Synonyms Detection

The aim of this paper is to present some results on word embeddings for the Romanian language, based on the word2vec method. More concretely, we generate word embeddings of different lengths, and using different preprocessing and training techniques. The embeddings are general purpose, and we use the Romanian language version of Wikipedia as corpus. We also evaluate the computational resources needed for the task. The embeddings are validated by performing some experiments on synonyms detection, using a new dataset created for this purpose. The code and the dataset are made publicly available. The results indicate that these types of embeddings can be used with the summarization approaches.