  We present an information retrieval based reverse dictionary system using
modern pre-trained language models and approximate nearest neighbors search
algorithms. The proposed approach is applied to an existing Estonian language
lexicon resource, S\~onaveeb (word web), with the purpose of enhancing and
enriching it by introducing cross-lingual reverse dictionary functionality
powered by semantic search.
  The performance of the system is evaluated using both an existing labeled
English dataset of words and definitions that is extended to contain also
Estonian and Russian translations, and a novel unlabeled evaluation approach
that extracts the evaluation data from the lexicon resource itself using
synonymy relations.
  Evaluation results indicate that the information retrieval based semantic
search approach without any model training is feasible, producing median rank
of 1 in the monolingual setting and median rank of 2 in the cross-lingual
setting using the unlabeled evaluation approach, with models trained for
cross-lingual retrieval and including Estonian in their training data showing
superior performance in our particular task.
