Classification of English Words into Grammatical Notations Using Deep Learning Technique

The impact of artificial intelligence (AI) on English language learning has become the center of attention in the past few decades. This study, with its potential to transform English language instruction and offer various instructional approaches, provides valuable insights and knowledge. To fully...

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Main Authors: Muhammad Imran, Sajjad Hussain Qureshi, Abrar Hussain Qureshi, Norah Almusharraf
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Information
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Online Access:https://www.mdpi.com/2078-2489/15/12/801
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author Muhammad Imran
Sajjad Hussain Qureshi
Abrar Hussain Qureshi
Norah Almusharraf
author_facet Muhammad Imran
Sajjad Hussain Qureshi
Abrar Hussain Qureshi
Norah Almusharraf
author_sort Muhammad Imran
collection DOAJ
description The impact of artificial intelligence (AI) on English language learning has become the center of attention in the past few decades. This study, with its potential to transform English language instruction and offer various instructional approaches, provides valuable insights and knowledge. To fully grasp the potential advantages of AI, more research is needed to improve, validate, and test AI algorithms and architectures. Grammatical notations provide a word’s information to the readers. If a word’s images are properly extracted and categorized using a CNN, it can help non-native English speakers improve their learning habits. The classification of parts of speech into different grammatical notations is the major problem that non-native English learners face. This situation stresses the need to develop a computer-based system using a machine learning algorithm to classify words into proper grammatical notations. A convolutional neural network (CNN) was applied to classify English words into nine classes: noun, pronoun, adjective, determiner, verb, adverb, preposition, conjunction, and interjection. A simulation of the selected model was performed in MATLAB. The model achieved an overall accuracy of 97.22%. The CNN showed 100% accuracy for pronouns, determiners, verbs, adverbs, and prepositions; 95% for nouns, adjectives, and conjunctions; and 90% for interjections. The significant results (<i>p</i> < 0.0001) of the chi-square test supported the use of the CNN by non-native English learners. The proposed approach is an important source of word classification for non-native English learners by putting the word image into the model. This not only helps beginners in English learning but also helps in setting standards for evaluating documents.
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spelling doaj-art-2084467b58e240c2a1b0eafe11d912ca2024-12-27T14:30:52ZengMDPI AGInformation2078-24892024-12-01151280110.3390/info15120801Classification of English Words into Grammatical Notations Using Deep Learning TechniqueMuhammad Imran0Sajjad Hussain Qureshi1Abrar Hussain Qureshi2Norah Almusharraf3Education Research Lab, Prince Sultan University, Riyadh 11586, Saudi ArabiaDepartment of Information Technology, Islamia University, Bahawalpur 06314, PakistanDepartment of English, University of Sahiwal, Sahiwal 57040, PakistanEducation Research Lab, Prince Sultan University, Riyadh 11586, Saudi ArabiaThe impact of artificial intelligence (AI) on English language learning has become the center of attention in the past few decades. This study, with its potential to transform English language instruction and offer various instructional approaches, provides valuable insights and knowledge. To fully grasp the potential advantages of AI, more research is needed to improve, validate, and test AI algorithms and architectures. Grammatical notations provide a word’s information to the readers. If a word’s images are properly extracted and categorized using a CNN, it can help non-native English speakers improve their learning habits. The classification of parts of speech into different grammatical notations is the major problem that non-native English learners face. This situation stresses the need to develop a computer-based system using a machine learning algorithm to classify words into proper grammatical notations. A convolutional neural network (CNN) was applied to classify English words into nine classes: noun, pronoun, adjective, determiner, verb, adverb, preposition, conjunction, and interjection. A simulation of the selected model was performed in MATLAB. The model achieved an overall accuracy of 97.22%. The CNN showed 100% accuracy for pronouns, determiners, verbs, adverbs, and prepositions; 95% for nouns, adjectives, and conjunctions; and 90% for interjections. The significant results (<i>p</i> < 0.0001) of the chi-square test supported the use of the CNN by non-native English learners. The proposed approach is an important source of word classification for non-native English learners by putting the word image into the model. This not only helps beginners in English learning but also helps in setting standards for evaluating documents.https://www.mdpi.com/2078-2489/15/12/801artificial intelligencegrammatical notationsCNNdeep learning
spellingShingle Muhammad Imran
Sajjad Hussain Qureshi
Abrar Hussain Qureshi
Norah Almusharraf
Classification of English Words into Grammatical Notations Using Deep Learning Technique
Information
artificial intelligence
grammatical notations
CNN
deep learning
title Classification of English Words into Grammatical Notations Using Deep Learning Technique
title_full Classification of English Words into Grammatical Notations Using Deep Learning Technique
title_fullStr Classification of English Words into Grammatical Notations Using Deep Learning Technique
title_full_unstemmed Classification of English Words into Grammatical Notations Using Deep Learning Technique
title_short Classification of English Words into Grammatical Notations Using Deep Learning Technique
title_sort classification of english words into grammatical notations using deep learning technique
topic artificial intelligence
grammatical notations
CNN
deep learning
url https://www.mdpi.com/2078-2489/15/12/801
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AT abrarhussainqureshi classificationofenglishwordsintogrammaticalnotationsusingdeeplearningtechnique
AT norahalmusharraf classificationofenglishwordsintogrammaticalnotationsusingdeeplearningtechnique