Assessing English language sentences readability using machine learning models
Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with e...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-01-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-818.pdf |
| _version_ | 1798035196663037952 |
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| author | Shazia Maqsood Abdul Shahid Muhammad Tanvir Afzal Muhammad Roman Zahid Khan Zubair Nawaz Muhammad Haris Aziz |
| author_facet | Shazia Maqsood Abdul Shahid Muhammad Tanvir Afzal Muhammad Roman Zahid Khan Zubair Nawaz Muhammad Haris Aziz |
| author_sort | Shazia Maqsood |
| collection | DOAJ |
| description | Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today’s information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner. |
| first_indexed | 2024-04-11T20:54:53Z |
| format | Article |
| id | doaj.art-9487469f6cf547ca8d8efc10af3d22c2 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-11T20:54:53Z |
| publishDate | 2022-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-9487469f6cf547ca8d8efc10af3d22c22022-12-22T04:03:43ZengPeerJ Inc.PeerJ Computer Science2376-59922022-01-017e81810.7717/peerj-cs.818Assessing English language sentences readability using machine learning modelsShazia Maqsood0Abdul Shahid1Muhammad Tanvir Afzal2Muhammad Roman3Zahid Khan4Zubair Nawaz5Muhammad Haris Aziz6Institute of Computing, Kohat University of Science and Technology, Kohat, KPK, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, KPK, PakistanNAMAL Institue of Mianwali, Mianwali, Punjab, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, KPK, PakistanRobotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi ArabiaDepartment of Data Science, Faculty of Computing and Information Technology, University of the Punjab, Lahore, Punjab, PakistanMechanical Engineering Department, University of Sargodha, Sargodha, Sargodha, Punjab, PakistanReadability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today’s information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner.https://peerj.com/articles/cs-818.pdfSentence readabilityFlesch-KincaidLanguage learningMachine learningNatural language processing |
| spellingShingle | Shazia Maqsood Abdul Shahid Muhammad Tanvir Afzal Muhammad Roman Zahid Khan Zubair Nawaz Muhammad Haris Aziz Assessing English language sentences readability using machine learning models PeerJ Computer Science Sentence readability Flesch-Kincaid Language learning Machine learning Natural language processing |
| title | Assessing English language sentences readability using machine learning models |
| title_full | Assessing English language sentences readability using machine learning models |
| title_fullStr | Assessing English language sentences readability using machine learning models |
| title_full_unstemmed | Assessing English language sentences readability using machine learning models |
| title_short | Assessing English language sentences readability using machine learning models |
| title_sort | assessing english language sentences readability using machine learning models |
| topic | Sentence readability Flesch-Kincaid Language learning Machine learning Natural language processing |
| url | https://peerj.com/articles/cs-818.pdf |
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