Quantifying the difficulty of word guessing based on lexical categorization

DOI

10.2991/978-94-6463-304-7_74How to use a DOI?

Keywords

Word Attribute Categorization; Susceptible Infected Recovered; N-array; Information Entropy

Abstract

This article focuses on the Wordle game, a word puzzle game that has become extremely popular on social media. To improve the user’s gaming experience, three models have been proposed to solve the problem: a model for predicting the number of players, a model for predicting the number of attempts, and a model for classifying word difficulty. For the problem of predicting the number of players, the SIR (Susceptible Infected Recovered) model was proposed. The research findings demonstrate that the N-array tree model exhibits a certain level of effectiveness in predicting the distribution of player attempt counts. The frequency of player word guesses and the prevalence of vocabulary play a significant role in the prediction process. Finally, this paper contributes to the difficulty classification of words based on IE (Information Entropy) model, and the experimental results showed that comparing the historical data with the corresponding information entropy would obtain an absolute error of 17%, which has a high degree of confidence.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shuhan Liu
AU  - Shuhan Xu
AU  - Yanqi Huang
PY  - 2023
DA  - 2023/12/04
TI  - Quantifying the difficulty of word guessing based on lexical categorization
BT  - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023)
PB  - Atlantis Press
SP  - 711
EP  - 716
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-304-7_74
DO  - 10.2991/978-94-6463-304-7_74
ID  - Liu2023
ER  -