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

TL;DR

This paper explores applying word2vec models to chess game data, treating move sequences as text, to analyze the game's structure and information representation from an academic perspective.

Contribution

It demonstrates how linguistic embedding techniques can be adapted to non-linguistic data like chess, revealing insights into the game's informational structure.

Findings

Word embeddings can be applied to chess move sequences.

Such models capture meaningful aspects of chess data.

The approach offers a novel perspective on game analysis.

Abstract

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.