You shall know a piece by the company it keeps. Chess plays as a data for word2vec models
This is an incremental academic exploration of applying NLP techniques to chess data, with no practical impact for players or engines.
The paper applies word2vec embeddings to chess game notations as a non-linguistic dataset, showing that these methods capture intrinsic patterns in chess but do not improve move selection or performance.
In this paper, I apply linguistic methods of analysis to non-linguistic data, chess plays, metaphorically equating one with the other and seeking analogies. Chess game notations are also a kind of text, and one can consider the records of moves or positions of pieces as words and statements in a certain language. In this article I show how word embeddings (word2vec) can work on chess game texts instead of natural language texts. I don't see how this representation of chess data can be used productively. It's unlikely that these vector models will help engines or people choose the best move. But in a purely academic sense, it's clear that such methods of information representation capture something important about the very nature of the game, which doesn't necessarily lead to a win.