你应通过其相伴之物来了解一物。国际象棋对局作为word2vec模型的数据
You shall know a piece by the company it keeps. Chess plays as a data for word2vec models
AI总结：
本文将语言学分析方法应用于国际象棋对局数据，展示word2vec词嵌入模型如何处理国际象棋对局文本，指出该方法虽未必能帮助选择最佳棋步，但在纯学术意义上捕捉到了游戏本质的某些重要特征。
摘要：
本文将语言学分析方法应用于非语言数据——国际象棋对局，隐喻地将二者等同并寻求类比。国际象棋对局记号也是一种文本，可以将棋步或棋子位置的记录视为某种语言中的词汇和语句。本文展示了词嵌入（word2vec）如何在国际象棋对局文本而非自然语言文本上运作。我不清楚这种国际象棋数据的表示方式如何能被富有成效地使用。这些向量模型不太可能帮助引擎或人类选择最佳棋步。但在纯学术意义上，很明显这种信息表示方法捕捉到了游戏本质中某些重要的东西，而这未必能带来胜利。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
