2025 IJCNLP IJCNLP 2025

Semantic, Orthographic, and Phonological Biases in Humans’ Wordle Gameplay

Abstract

AbstractWe show that human players’ gameplay in the game of Wordle is influenced by the semantics, orthography, and phonology of the player’s previous guesses. We compare actual human players’ guesses with near-optimal guesses using NLP techniques. We study human language use in the constrained environment of Wordle, which is situated between natural language use and the artificial word association task.

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio