Summary of You Shall Know a Piece by the Company It Keeps. Chess Plays As a Data For Word2vec Models, By Boris Orekhov
You shall know a piece by the company it keeps. Chess plays as a data for word2vec models
by Boris Orekhov
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of linguistic analysis methods to non-linguistic data, specifically chess plays. By treating chess game notations as text, researchers can leverage word embeddings (word2vec) on this unique dataset. The study demonstrates how these techniques can capture essential aspects of the game’s nature, but questions their practical utility in improving gameplay. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper takes a fascinating approach by applying linguistic methods to chess games. It shows that word embeddings can work on chess data too! This might not help humans or computers make better moves, but it’s an interesting way to understand the essence of the game. |
Keywords
» Artificial intelligence » Word2vec
