Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game

Yifei Hu; Xinyao Zhuang; Yuxin Wan; Nanlin Jin; Xiaohui Zhu

doi:10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106

Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game

Yifei Hu
, Xinyao Zhuang
, Yuxin Wan
, Nanlin Jin^*
, Xiaohui Zhu^*

^*Corresponding author for this work

Department of Computing

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

This paper introduces the Word Orthography and Relationship-Dominant Engineering (WOR-De) model for predicting the distribution of Wordle game completion across the number of attempts (DWCA). Our model employs five feature engineering methods: Word Frequency Referencing (WFR), Orthographic Feature Extraction (OFE), Respective Adjacency Probability (RAP), Dominant Pattern Matching (DPM), and Edit Distance Counting (EDC). These methods collaboratively transform each input word into a five-dimensional vector that encapsulates intrinsic attributes like frequency, adjacency, and commonalities. This vector serves as a specialized word representation within the WOR-De model. When integrated with machine learning algorithms, the WOR-De model can effectively forecast the DWCA. Comparative analyses with established word embedding models like GloVe and FastText demonstrate that WOR-De excels in capturing both semantic elements essential for such Wordle game prediction.

Original language	English
Title of host publication	2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	563-569
Number of pages	7
ISBN (Electronic)	9798350309461
DOIs	https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106
Publication status	Published - May 2024
Event	2023 IEEE Congress on Cybermatics: 16th IEEE International Conferences on Internet of Things, iThings 2023, 19th IEEE International Conference on Green Computing and Communications, GreenCom 2023, 16th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2023 and 9th IEEE International Conference on Smart Data, SmartData 2023 - Danzhou, China Duration: 17 Dec 2023 → 21 Dec 2023

Publication series

Name	Proceedings - IEEE Congress on Cybermatics: 2023 IEEE International Conferences on Internet of Things, iThings 2023, IEEE Green Computing and Communications, GreenCom 2023, IEEE Cyber, Physical and Social Computing, CPSCom 2023 and IEEE Smart Data, SmartData 2023

Conference

Conference	2023 IEEE Congress on Cybermatics: 16th IEEE International Conferences on Internet of Things, iThings 2023, 19th IEEE International Conference on Green Computing and Communications, GreenCom 2023, 16th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2023 and 9th IEEE International Conference on Smart Data, SmartData 2023
Country/Territory	China
City	Danzhou
Period	17/12/23 → 21/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Feature engineering
Machine learning
Natural language processing
Word embedding
Wordle

Access to Document

10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106

Cite this

Hu, Y., Zhuang, X., Wan, Y., Jin, N., & Zhu, X. (2024). Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game. In 2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) (pp. 563-569). (Proceedings - IEEE Congress on Cybermatics: 2023 IEEE International Conferences on Internet of Things, iThings 2023, IEEE Green Computing and Communications, GreenCom 2023, IEEE Cyber, Physical and Social Computing, CPSCom 2023 and IEEE Smart Data, SmartData 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106

Hu, Yifei ; Zhuang, Xinyao ; Wan, Yuxin et al. / Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game. 2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). Institute of Electrical and Electronics Engineers Inc., 2024. pp. 563-569 (Proceedings - IEEE Congress on Cybermatics: 2023 IEEE International Conferences on Internet of Things, iThings 2023, IEEE Green Computing and Communications, GreenCom 2023, IEEE Cyber, Physical and Social Computing, CPSCom 2023 and IEEE Smart Data, SmartData 2023).

@inproceedings{703a6b1a1e3c4fdb914d02aa2fb0413c,

title = "Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game",

abstract = "This paper introduces the Word Orthography and Relationship-Dominant Engineering (WOR-De) model for predicting the distribution of Wordle game completion across the number of attempts (DWCA). Our model employs five feature engineering methods: Word Frequency Referencing (WFR), Orthographic Feature Extraction (OFE), Respective Adjacency Probability (RAP), Dominant Pattern Matching (DPM), and Edit Distance Counting (EDC). These methods collaboratively transform each input word into a five-dimensional vector that encapsulates intrinsic attributes like frequency, adjacency, and commonalities. This vector serves as a specialized word representation within the WOR-De model. When integrated with machine learning algorithms, the WOR-De model can effectively forecast the DWCA. Comparative analyses with established word embedding models like GloVe and FastText demonstrate that WOR-De excels in capturing both semantic elements essential for such Wordle game prediction.",

keywords = "Feature engineering, Machine learning, Natural language processing, Word embedding, Wordle",

author = "Yifei Hu and Xinyao Zhuang and Yuxin Wan and Nanlin Jin and Xiaohui Zhu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Congress on Cybermatics: 16th IEEE International Conferences on Internet of Things, iThings 2023, 19th IEEE International Conference on Green Computing and Communications, GreenCom 2023, 16th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2023 and 9th IEEE International Conference on Smart Data, SmartData 2023 ; Conference date: 17-12-2023 Through 21-12-2023",

year = "2024",

month = may,

doi = "10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106",

language = "English",

series = "Proceedings - IEEE Congress on Cybermatics: 2023 IEEE International Conferences on Internet of Things, iThings 2023, IEEE Green Computing and Communications, GreenCom 2023, IEEE Cyber, Physical and Social Computing, CPSCom 2023 and IEEE Smart Data, SmartData 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "563--569",

booktitle = "2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing \& Communications (GreenCom) and IEEE Cyber, Physical \& Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)",

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Hu, Y, Zhuang, X, Wan, Y, Jin, N & Zhu, X 2024, Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game. in 2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). Proceedings - IEEE Congress on Cybermatics: 2023 IEEE International Conferences on Internet of Things, iThings 2023, IEEE Green Computing and Communications, GreenCom 2023, IEEE Cyber, Physical and Social Computing, CPSCom 2023 and IEEE Smart Data, SmartData 2023, Institute of Electrical and Electronics Engineers Inc., pp. 563-569, 2023 IEEE Congress on Cybermatics: 16th IEEE International Conferences on Internet of Things, iThings 2023, 19th IEEE International Conference on Green Computing and Communications, GreenCom 2023, 16th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2023 and 9th IEEE International Conference on Smart Data, SmartData 2023, Danzhou, China, 17/12/23. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106

Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game. / Hu, Yifei; Zhuang, Xinyao; Wan, Yuxin et al.
2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). Institute of Electrical and Electronics Engineers Inc., 2024. p. 563-569 (Proceedings - IEEE Congress on Cybermatics: 2023 IEEE International Conferences on Internet of Things, iThings 2023, IEEE Green Computing and Communications, GreenCom 2023, IEEE Cyber, Physical and Social Computing, CPSCom 2023 and IEEE Smart Data, SmartData 2023).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game

AU - Hu, Yifei

AU - Zhuang, Xinyao

AU - Wan, Yuxin

AU - Jin, Nanlin

AU - Zhu, Xiaohui

PY - 2024/5

Y1 - 2024/5

N2 - This paper introduces the Word Orthography and Relationship-Dominant Engineering (WOR-De) model for predicting the distribution of Wordle game completion across the number of attempts (DWCA). Our model employs five feature engineering methods: Word Frequency Referencing (WFR), Orthographic Feature Extraction (OFE), Respective Adjacency Probability (RAP), Dominant Pattern Matching (DPM), and Edit Distance Counting (EDC). These methods collaboratively transform each input word into a five-dimensional vector that encapsulates intrinsic attributes like frequency, adjacency, and commonalities. This vector serves as a specialized word representation within the WOR-De model. When integrated with machine learning algorithms, the WOR-De model can effectively forecast the DWCA. Comparative analyses with established word embedding models like GloVe and FastText demonstrate that WOR-De excels in capturing both semantic elements essential for such Wordle game prediction.

AB - This paper introduces the Word Orthography and Relationship-Dominant Engineering (WOR-De) model for predicting the distribution of Wordle game completion across the number of attempts (DWCA). Our model employs five feature engineering methods: Word Frequency Referencing (WFR), Orthographic Feature Extraction (OFE), Respective Adjacency Probability (RAP), Dominant Pattern Matching (DPM), and Edit Distance Counting (EDC). These methods collaboratively transform each input word into a five-dimensional vector that encapsulates intrinsic attributes like frequency, adjacency, and commonalities. This vector serves as a specialized word representation within the WOR-De model. When integrated with machine learning algorithms, the WOR-De model can effectively forecast the DWCA. Comparative analyses with established word embedding models like GloVe and FastText demonstrate that WOR-De excels in capturing both semantic elements essential for such Wordle game prediction.

KW - Feature engineering

KW - Machine learning

KW - Natural language processing

KW - Word embedding

KW - Wordle

UR - https://www.scopus.com/pages/publications/85192852997

U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106

DO - 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106

M3 - Conference Proceeding

AN - SCOPUS:85192852997

T3 - Proceedings - IEEE Congress on Cybermatics: 2023 IEEE International Conferences on Internet of Things, iThings 2023, IEEE Green Computing and Communications, GreenCom 2023, IEEE Cyber, Physical and Social Computing, CPSCom 2023 and IEEE Smart Data, SmartData 2023

SP - 563

EP - 569

BT - 2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Congress on Cybermatics: 16th IEEE International Conferences on Internet of Things, iThings 2023, 19th IEEE International Conference on Green Computing and Communications, GreenCom 2023, 16th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2023 and 9th IEEE International Conference on Smart Data, SmartData 2023

Y2 - 17 December 2023 through 21 December 2023

ER -

Hu Y, Zhuang X, Wan Y, Jin N , Zhu X. Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game. In 2023 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). Institute of Electrical and Electronics Engineers Inc. 2024. p. 563-569. (Proceedings - IEEE Congress on Cybermatics: 2023 IEEE International Conferences on Internet of Things, iThings 2023, IEEE Green Computing and Communications, GreenCom 2023, IEEE Cyber, Physical and Social Computing, CPSCom 2023 and IEEE Smart Data, SmartData 2023). doi: 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics60724.2023.00106

Word Orthography and Relationship-Dominant Engineering (WOR-De) Model for Wordle Game

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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