Abstract
The chess domain is well-suited for creating an artificial intelligence (AI) system that mimics real-world challenges, including decision-making. Throughout the years, minimal attention has been paid to investigating insights derived from unstructured chess data sources. In this study, we examine the complicated relationships between multiple referenced moves in a chess-teaching textbook, and propose a novel method designed to encapsulate chess knowledge derived from move-action phrases. This study investigates the feasibility of using a modified sentiment analysis method as a means for evaluating chess moves based on text. Our proposed Aspect-Based Sentiment Analysis (ABSA) method represents an advancement in evaluating the sentiment associated with referenced chess moves. By extracting insights from move-action phrases, our approach aims to provide a more fine-grained and contextually aware ‘chess move’-based sentiment classification. Through empirical experiments and analysis, we evaluate the performance of our fine-tuned ABSA model, presenting results that confirm the efficiency of our approach in advancing aspect-based sentiment classification within the chess domain. This research contributes to the area of game-playing by machines and shows the practical applicability of leveraging NLP techniques to understand the context of strategic games. Keywords: Natural Language Processing, Chess, Aspect-based Sentiment Analysis (ABSA), Chess Move Evaluation.- Anthology ID:
- 2024.games-1.5
- Volume:
- Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Chris Madge, Jon Chamberlain, Karen Fort, Udo Kruschwitz, Stephanie Lukin
- Venues:
- games | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 32–42
- Language:
- URL:
- https://aclanthology.org/2024.games-1.5
- DOI:
- Cite (ACL):
- Haifa Alrdahi and Riza Batista-Navarro. 2024. Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks. In Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024, pages 32–42, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks (Alrdahi & Batista-Navarro, games-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.games-1.5.pdf