Optimising word embeddings with search-based approaches

Word embeddings have rapidly become an all-purpose tool for a diverse range of real world applications. This development is nurtured by the availability and applicability of pre-trained models. However, their usage faces the risk of being inaccurate when used in domains different from the ones they were trained on.