Exploring linguistic variation in Pakistani English newspaper editorials through multidimensional analysis

Fatima, Mahrukh; Siddique, Ali Raza; Ahmad, Muhammad; Mahmood, Muhammad Asim

doi:10.1177/07395329231194005

Newspaper Research Journal

2023

DOI: 10.1177/07395329231194005

|View full text |Cite

Exploring linguistic variation in Pakistani English newspaper editorials through multidimensional analysis

Mahrukh Fatima,

Ali Raza Siddique,

Muhammad Ahmad

et al.

Abstract: This research investigated linguistic variation in Pakistani English newspaper editorials employing multidimensional analysis to identify: distinguished language feature; and linguistic variation in the light of textual dimensions model. Corpus comprised 1,000 editorials from four newspapers. Results showed the distinguished use of nouns, public verbs and sentence relatives thereby the editorials were found focusing on narrative, explicit and informational discourse. These results led to conclude about the pre… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 42 publications

(85 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Analyzing Linguistic Variations In The Discussion Sections Of Pakistani English Research Articles: A Multidimensional Study

Pervez,

Siddique,

Ahmad

2024

Linguistica Silesiana

View full text Add to dashboard Cite

This study investigated linguistic variations to establish the legitimacy of Pakistani English as a distinct variety. The corpus comprised 70 discussion sections from Pakistani English research articles distributed across three academic disciplines (engineering, information technology, and literature). The analysis was conducted using Multidimensional Analysis Tagger v.1.1, which scrutinized 67 grammatical features across five dimensions. The results revealed significant differences in dimensions 1, 3, and 5 which indicated variations in Pakistani English. The results also showed the discussion sections: being highly informative, formal, abstract, and explicit in engineering; informative, and abstract in information technology; and less informative, explicit, and abstract in literature research articles. However, the results displayed fewer variations in dimensions 2, and 4 which suggested them being interesting for further research. Overall, this research contributed to our understanding of the linguistic variations in Pakistani English and its distinct characteristics across academic disciplines.

show abstract

Analyzing Linguistic Variations In The Discussion Sections Of Pakistani English Research Articles: A Multidimensional Study

Pervez,

Siddique,

Ahmad

2024

Linguistica Silesiana

View full text Add to dashboard Cite

show abstract

Classification of English Words into Grammatical Notations Using Deep Learning Technique

Imran,

Qureshi,

Qureshi

et al. 2024

Information

View full text Add to dashboard Cite

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 (p < 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.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Exploring linguistic variation in Pakistani English newspaper editorials through multidimensional analysis

Cited by 2 publications

References 42 publications

Analyzing Linguistic Variations In The Discussion Sections Of Pakistani English Research Articles: A Multidimensional Study

Analyzing Linguistic Variations In The Discussion Sections Of Pakistani English Research Articles: A Multidimensional Study

Classification of English Words into Grammatical Notations Using Deep Learning Technique

Contact Info

Product

Resources

About