The holidays are coming, and we are ready to release a new version of our service: RusVectōrēs 2.0. You may consider it as a gift to all the users and all the people interested in distributional semantics.
For those who were not aware of our service: : RusVectōrēs computes semantic relations between words in Russian.
How is that done? In distributional semantics, words are usually represented as vectors in a multi-dimensional space of their contexts. Semantic similarity between two words is then trivially calculated as a cosine similarity between their corresponding vectors; it takes values between -1 and 1. 0 value means the words lack similar contexts, and thus their meanings are unrelated to each other. 1 value means that the words' contexts are absolutely identical, and thus their meaning is very similar.
RusVectōrēs allows to work with word vectors in the neural embedding models we trained on Russian National Corpus, news corpus and web corpus. Users can compute semantic associates of a given word, find a cosine similarity coefficient between a pair of words, perform simple algebraic operations on vectors. The models are trained using Skip-Gram and CBOW algorithms introduced in a well known word2vec tool.
We previously presented our service at the workshop "Quantitative Approaches to the Russian Language" in Helsinki in August and at the AINL-FRUCT tutorial on distributional semantics in Saint-Peterburg in November. Since then, 0we have significantly improved RusVectōrēs services, and you have even more possibilities for research! The main changes in the new release are the following::
We wish that your research were unlimited by the complexity of computations! Happy holidays!