Insight into wordle's data set based on deep learning

Jia Song; Shuwei Peng; Haopeng Du; Guitang Wang

doi:10.1117/12.2682565

20 June 2023 Insight into wordle's data set based on deep learning

Jia Song, Shuwei Peng, Haopeng Du, Guitang Wang

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Proceedings Volume 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023); 1271523 (2023) https://doi.org/10.1117/12.2682565
Event: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 2023, Dalian, China

Abstract

Nowadays, Wordle became almost everyone's current obsession. To study the reason for Wordle’s explosion, look for the secret behind Wordle. It is beneficial to develop a forecasting model to measure the fluctuations and distributions of the results based on time series and words. In the text used the context processing of words in text sequences in natural language processing to analogize that the same rule can be used for the composition and structure of words, so as to establish a percentage prediction model for the number of attempts of players with the character mechanism of letter position and structure in words. The error uncertainty of the model is evaluated by the MAPE error value. Through the analysis of the MAPE value, the error of the model to the predicted value is about 1.92%, so it is confident that the model can complete the prediction task with an error not exceeding 1.92%. Through this model, Predicting the result of the word "EERIE" as (2.16, 10.90 14.06, 24.49, 25.79, 14.41, 3.45).

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Jia Song, Shuwei Peng, Haopeng Du, and Guitang Wang "Insight into wordle's data set based on deep learning", Proc. SPIE 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 1271523 (20 June 2023); https://doi.org/10.1117/12.2682565

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KEYWORDS

Data modeling

Deep learning

Education and training

Error analysis

Data processing

Machine learning

Matrices

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