You shall know a piece by the company it keeps. Chess plays as a data for word2vec models

In this paper, I apply linguistic methods of analysis to non-linguistic data, metaphorically equating one with the other and seeking analogies. The productivity of this approach has been proven within the field of Super Linguistics. I argue that developed by computational linguists word embeddings (made with the algorithm word2vec) can shed light on the features of chess moves. Recently, computational linguistics has made a great progress in natural language processing (NLP). Within this field, tools have been developed for machine analysis of morphology, syntax and semantics. Computational linguistics became a stand-alone research area with its conferences (weblink) and actual fields. The experts have developed general principles of text analysis, optimal methods of word counting.