You shall know a piece by the company it keeps Chess plays as a data for word2vec models
<|reference_start|>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, 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.<|reference_end|>
@article{orekhov2024you,
    title={You shall know a piece by the company it keeps. Chess plays as a data
  for word2vec models},
    author={Boris Orekhov},
    journal={arXiv preprint arXiv:2407.19600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19600},
    primaryClass={cs.CL cs.AI}
}