National Language Of Pakistan Research Articles

زبان کے معیار کے تعین کی ضرورت و اہمیت اور میڈیا کا کردار

Debates in setting benchmarks for a specific language have great significance which covers the areas like pronunciation, accent, tone and idioms etc. As Urdu is national language of Pakistan. It is need of the hour to establish certain parameters and may be incorporated in different spheres of life to create uniformity in oral as well as in written communication like diction and orthography. In this way a standard may be evolved in textbooks, electronic media and print media. In this article, researcher discusses the issues in Pakistan perspective and proposes certain recommendations to fix the problem.

Reliability and validity estimation of Urdu version of Children Emotion Management Scales (CEMS) in Pakistan.

The present study aimed to translate and validate Children's Emotion Management Scales into Urdu, the national language of Pakistan. The current study comprised three different phases, i.e., phase I: Cross-language validation over a sample of (N = 169) school children, estimated at a 1-week interval. The results indicate a significant correlation (r = 0.846-0.891) at p < 0.01. In phase II, the internal consistency reliability (r = 0.808-0.904) and split-half reliability (r = 0.737-0.898) of the scale were assessed (N = 683) at p < 0.01. Furthermore, significant results for test-retest reliability analysis (N = 168) were obtained (r = 0.736-0.917 at p < 0.01), following the confirmatory factor analysis (CFA) (N = 1,083). Exploratory factor analysis (EFA) was conducted on the same sample chosen for CFA. EFA resulted in the retention of original inhibition (INH), dysregulated expression (DYS), and emotional coping (EMO) factors. CFA findings suggest a good model fit. In phase III, convergent validity and divergent validity were checked (N = 385, 255, and 213). Convergent validity of INH and DYS subscales and divergent validity of EMO subscales were established, with SBI (r = 0.217-0.609; 0.210-0.445; -0.026 to -0.553), SHS (r = 0.417-0.441; 0.480-0.546; -0.338 to -0.582), and suppression subscale of ERQ (r = 0.430-0.480; 0.468-0.522; -0.245 to -0.369) at p < 0.01. For divergent validity of INH and DYS subscales and convergent validity of EMO subscales, their scores were correlated with the SPS (r = -0.204 to -0.350; -0.318 to -0.459; 0.191-0.531), RSE Scale (r = -0.226 to -0.351; -0.279 to -0.352; 0.255-0.507), DTS (-0.290 to -0.617; -0.369 to -0.456; 0.246-0.680), and reappraisal subscale of ERQ (r = -0.456 to -0.541; -0.329 to -0.544; 0.446-0.601) at p < 0.01. It is concluded that the scale is reliable and valid with sound psychometric properties.

Reliability and validity of Urdu PARENTS for assessing non-technical skills of paediatric residents in a teaching hospital in Pakistan

PurposeThe primary objective of our study is twofold. First, we assessed nontechnical skills (NTSs), such as the cognitive, social, and personal skills of postgraduate residents (PGRs), from paediatric caregivers’ perspectives in a paediatric emergency department (PED) of a tertiary care hospital. Second, we evaluated the reliability and validity of the ‘Parents’ Assessment of Residents Enacting Non-Technical Skills’ (PARENTS) instrument in its Urdu-translated version, ensuring its applicability and accuracy in the Pakistani context.Materials and methodsThis mixed-method study used an instrument translation and validation design. We translated an existing instrument, PARENTS, into Urdu, the national language of Pakistan, and administered it to paediatric caregivers in the PED of a tertiary care hospital. We collected data from 471 paediatric caregivers and coded them for analysis in AMOS and SPSS.ResultsThe Urdu-translated version of the PARENTS demonstrated reliability and internal validity in our study. The findings from the assessment revealed that paediatric caregivers expressed satisfaction with the knowledge and skill of residents. However, there was comparatively lower satisfaction regarding the residents’ display of patience or empathy towards the children under their care.ConclusionThe study findings support the validity and reliability of the PARENTS as an effective instrument for assessing the NTS of PGRs from the perspective of paediatric caregivers. With its demonstrated efficacy, medical educators can utilize PARENTS to pinpoint specific areas that require attention regarding the NTS of PGRs, thus facilitating targeted interventions for enhanced patient care outcomes.

Sentiment Analysis of Roman Text: Challenges, Opportunities, and Future Directions

The rise of social media platforms has sparked significant interest among Natural Language Processing (NLP) researchers in Sentiment Analysis over the past few decades. Numerous studies have focused on analyzing the sentiment of different languages, including Urdu, which is widely spoken in South Asia and serves as the national language of Pakistan. While considerable research has been conducted on sentiment analysis using Machine Learning (ML), Deep Learning (DL), and other methods, Roman Urdu sentiment analysis holds equal importance. This paper presents a comprehensive analysis of existing literature on sentiment analysis of various languages, with a specific focus on Roman text and approaches such as ML, DL, and others. Additionally, the paper proposes new directions for future researchers in this field.

Religious, Cultural and Lingual Stereotypes: a Deconstructive Study of Haji’s the Sweetness of Tears

This study aims at the deconstructive analysis of the religious, cultural and lingual stereotypes represented through the Americans and the Muslims in Nafisa Haji’s The Sweetness of Tears. Both Americans and Muslims have been sharing a binary of the victims and the victimizers since the incident of 9/11. Muslims are scrutinized and deported to their hometown on charge of being ‘terrorists’ by the Americans. Firstly, the study illustrates that by deconstructing these stereotypes, a new intimate association can be established between the Americans and the Muslims. Derrida has given the approach of ‘Deconstruction’ to equalize the power structures of binary oppositions. In his theory of ‘Deconstruction’, he deconstructs the unequal association of binaries as “good” and “evil” and “Superior” and “Inferior”. He says that these binaries are the expression of one another e.g “good” is the expression of “evil” and “evil” is the expression of “good”. Similarly, “Superior” is the expression of “Inferior” and “Inferior” is the expression of “Superior”. By deconstructing these stereotypes, the rigid bond of the Americans and the Muslims can be entered into a harmonious association. Secondly, the Muslims can maintain their identities as “Muslims” without tagging them as being terrorists. Thirdly, by crossing the boundaries of a Muslim country, Americans can learn and understand Muslims’ religion, culture and language as is shown in the novel under discussion. This study depicts that the Americans have started to accept the Muslims as non-violent people. Jo March (protagonist) learns Urdu (the national language of Pakistan), comes to Pakistan and lives here to know about the culture, traditions and religion of Muslims. She binds the ties together and made a harmonious association between the American family and the Muslim family. This study supports this intimate association between both Americans and Muslims.

THE STUDY TO EXAMINE THE DIFFICULTIES’ OF STUDENTS IN LEARNING ENGLISH AS A SECOND LANGUAGE IN GOVERNMENT HIGH SCHOOLS DISTRICT JAFFARABAD, SOHBATPUR AND NASEERABAD

Urdu is national language of Pakistan but English enjoys the status of second language. English is mandatory subject which is being taught from primary to university level in Pakistan. In Pakistani education system, after clearing secondary education, the whole subjects are being taught in English language at higher secondary, bachelor, graduation, master’s and doctorate level in all public and private educational institutions. The medium of instructions and medium of communication at higher education is in English language. The study focused on students in government high schools district Jaffarabad, Sohbatpur and Naseerabad Baluchistan faced by more difficulties in English language because school is existing in a rural area of Baluchistan. This research was examined the difficulties in learning English language in Baluchistan and researcher made proper suggestions that help students to make their learning easy in view point of Pakistani context of learning. Quantitative data was collected from survey questionnaire and qualitative data conducted through semistructure interview. Data was analyzed through SPSS, excel and thematic analysis The sample of investigation were collected from 240 students in government high schools. The students are facing different difficulties in different learning areas which need highly consideration to solved.

ISE-Hate: A benchmark corpus for inter-faith, sectarian, and ethnic hatred detection on social media in Urdu

Social media has become the most popular platform for free speech. This freedom of speech has given opportunities to the oppressed to raise their voice against injustices, but on the other hand, this has led to a disturbing trend of spreading hateful content of various kinds. Pakistan has been dealing with the issue of sectarian and ethnic violence for the last three decades and now due to freedom of speech, there is a growing trend of disturbing content about religion, sect, and ethnicity on social media. This necessitates the need for an automated system for the detection of controversial content on social media in Urdu which is the national language of Pakistan. The biggest hurdle that has thwarted the Urdu language processing is the scarcity of language resources, annotated datasets, and pretrained language models. In this study, we have addressed the problem of detecting Interfaith, Sectarian, and Ethnic hatred on social media in Urdu language using machine learning and deep learning techniques. In particular, we have: (1) developed and presented guidelines for annotating Urdu text with appropriate labels for two levels of classification, (2) developed a large dataset of 21,759 tweets using the developed guidelines and made it publicly available, and (3) conducted experiments to compare the performance of eight supervised machine learning and deep learning techniques, for the automated identification of hateful content. In the first step, experiments are performed for the hateful content detection as a binary classification task, and in the second step, the classification of Interfaith, Sectarian and Ethnic hatred detection is performed as a multiclass classification task. Overall, Bidirectional Encoder Representation from Transformers (BERT) proved to be the most effective technique for hateful content identification in Urdu tweets.

The Morphological Adaptation Process and Borrowing in Urdu Language

In Language is always subjected to changes and this dynamic process goes on. This process makes the languages more sophisticated and up to date. Language changes occur through many processes, word formation processes are the key processes among them. Borrowing and word's adaptation is the most common process through which languages evolve mostly in this global era as languages' contact takes place widely. Borrowing is the process in which one language borrows words from another language and adapts those words in its vocabulary. The Urdu language is the national language of Pakistan, as it has come in contact with different languages, and it has a wide capacity to adapt many words and borrow them. It has a rich phonetic system so it can easily own borrowed words. This paper analyzes the process of borrowing and words adaption in the Urdu language. The research has been carried out qualitatively by using a descriptive-analytical approach.

OBSOLETE WORDS IN URDU LANGUAGE

Urdu is the national language of Pakistan. It is a standardized register of Hindustani language and is lingua franca of Pakistan. Urdu is the official language of six Indian states. The study aims to investigate how language is dying out within the language the words are obsoleted from Urdu language. The target population for this study was Native speaker of Urdu language of 11-25 years age, 26-40 years age, 41-55 and 60 years &amp; above of age. The sample consisted of 20 people each group. The data was obtained through wordlist of 60 chosen words and then in the end, interviews were administered to decipher the language attrition in Urdu language. Triangulation approach was used for data analysis. The data of 65 respondents was analyzed using statistical tests in order to compare the performance of native and non-native speakers of Urdu language. Their motivation was also examined. Results revealed that in native speech community, the speakers of 41-55 age groups know many words along with its correct meanings and correct usage and the ratio was not that bad even in the group of participants who are aged 26-40. But in the group aged 11-25 the ratio is alarmingly low. The language is not transmitted to the next generation because the parents of this age group themselves did not acquire Urdu as their first language. Language is not documented in modern time that is why it is changing. There are many other words from other languages that are replacing the actual word of Urdu language. Speakers are using those words for convenience purpose as well. The need of the time is to document the language with its essence to prove its antiquity.

Urdu Sentiment Analysis

Abstract The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amount of work in the different domains has been done in numerous languages for sentiment analysis, but minimal research has been conducted on the national language of Pakistan, which is Urdu. Twitter users who are familiar with Urdu update the tweets in two different textual formats either in Urdu Script (Nastaleeq) or in Roman Urdu. Thus, the paper is an attempt to perform the sentiment analysis on the Urdu language by extracting the tweets (Nastaleeq and Roman Urdu both) from Twitter using Tweepy API. A machine learning-based approach has been adopted for this study and the tool opted for the purpose is WEKA. The best algorithm was identified based on evaluation metrics, which comprise the number of correctly and incorrectly classified instances, accuracy, precision, and recall. SMO was found to be the most suitable machine learning algorithm for performing the sentiment analysis on Urdu (Nastaleeq) tweets, while the Roman Urdu Random Forest algorithm was identified as the best one.

Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches.

AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition

The ongoing development of audio datasets for numerous languages has spurred research activities towards designing smart speech recognition systems. A typical speech recognition system can be applied in many emerging applications, such as smartphone dialing, airline reservations, and automatic wheelchairs, among others. Urdu is a national language of Pakistan and is also widely spoken in many other South Asian countries (e.g., India, Afghanistan). Therefore, we present a comprehensive dataset of spoken Urdu digits ranging from 0 to 9. Our dataset has 25,518 sound samples that are collected from 740 participants. To test the proposed dataset, we apply different existing classification algorithms on the datasets including Support Vector Machine (SVM), Multilayer Perceptron (MLP), and flavors of the EfficientNet. These algorithms serve as a baseline. Furthermore, we propose a convolutional neural network (CNN) for audio digit classification. We conduct the experiment using these networks, and the results show that the proposed CNN is efficient and outperforms the baseline algorithms in terms of classification accuracy.

Study of rare genetic variants in TM4SF20, NFXL1, CNTNAP2, and ATP2C2 in Pakistani probands and families with language impairment

Language impairment (LI) is highly heritable and aggregates in families. Genetic investigation of LI has revealed many chromosomal regions and genes of interest, though very few studies have focused on rare variant analysis in non-English speaking or non-European samples. We selected four candidate genes (TM4SF20, NFXL1, CNTNAP2 and ATP2C2) strongly suggested for specific language impairment (SLI), a subtype of LI, and investigated rare protein coding variants through Sanger sequencing of probands with LI ascertained from Pakistan. The probands and their family members completed a speech and language family history questionnaire and a vocabulary measure, the Peabody Picture Vocabulary Test-fourth edition (PPVT-4), translated to Urdu, the national language of Pakistan. Our study aimed to determine the significance of rare variants in these SLI candidate genes through segregation analysis in a novel population with a high rate of consanguinity. In total, we identified 16 rare variants (according to the rare MAF in the global population in gnomAD v2.1.1 database exomes), including eight variants with a MAF <0.5% in the South Asian population. Most of the identified rare variants aggregated in proband's families, one rare variant (c.*9 T > C in CNTNAP2) co-segregated in a small family (PKSLI-64) and another (c.2465C > T in ATP2C2) co-segregated in the proband branch (PKSLI-27). The lack of complete co-segregation of most of the identified rare variants indicates that while these genes could be involved in the overall risk for LI, other genes are likely involved in LI in this population. Future investigation of these consanguineous families has the potential to expand our understanding of gene function related to language acquisition and impairment.

Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Automatic Cyberbullying detection has remained very challenging task since social media content and conversations are usually posted in unstructured free-text form leaving behind the language norms. The major concern and gap in formulating cyberbullying detection strategies is scarcity of available linguistic resources typically for newly evolved languages. Roman Urdu has recently emerged and hence is a resource poor language. Urdu has been widely known as the national language of Pakistan. However, because of socio-cultural and multilingual aspects, Roman Urdu is used widely on the Internet by Asians and more specifically Pakistanis. To fulfil the above stated gap, this research work presents guidelines for data annotation process and developed two linguistic resources: (i) Annotated corpus in Roman Urdu Language for cyberaggression and offensive language detection. The process of data annotation involved bilingual annotators instead of crowdsourcing. It has the benefit of correctly annotating instances that constitute clear cases of cyberbullying without compromising data quality. The developed corpus is highly balanced (with almost negligible skew) unlike most of the existing corpuses even in mature languages. (ii) Processing textual information for NLP tasks involves Stop-word elimination as a sub phase. Stop words carry least semantic information and increase feature space as compared to the other tokens and index terms in corpora. We have developed domain specific stop words for Roman Urdu Language considering all the lexical variants and typically in the context of aggression detection and collected data. The work has been carried out using python programming language and Pycharm IDE.

Pahari Distinctiveness without Pahari Language: A Study of Language Attitudes of the Pahari Community

Pahari language spoken in Murree and its suburbs is in contact with Urdu, the national language of Pakistan. It is also in contact with Punjabi and English. The present study explores the views and attitudes of the Pahari speaking community regarding the Pahari identity and culture vis-à-vis languages like Pahari, Punjabi, Urdu, and English. The study is ethnographic and is based on detailed observation and in-depth interviews of a sample of 91 people belonging to three generations. The study concludes that Pahari speaking community especially the Abbasi tribe, on the whole, claims to protect their culture and identity but gradually each new generation is hesitant in continuing Pahari as the symbol of their identity. As a result, language is, to a great extent, shifting towards Urdu. On a minor level, its shift to English and Punjabi cannot be ignored. The paper also explores certain reasons latent behind this kind of attitude.

Urdu as an Official Language: A Constitutional Mandate Compliance and Challenges Prospectives

Though Urdu language has no religious background, it is inspired by Arabic a mother-tongue of Islam. However, after its development, Urdu was exclusively attributed to be the language of Muslims of the Sub-continent. They faced many challenges for the survival of Urdu during the Movement of Independence. For the same reason, after independence, Urdu was declared to be the National Language of Pakistan. Afterward, certain bodies were established for its proper implementation as official language of the country. This aspect is recognized as constitutional obligation in term of Article 251 of Constitution, 1973. Though, the government was given sufficient time for the compliance of the same, this task could not have been accomplished despite of lapse of almost fifty years. Even, the judicial verdicts could not make the concerned institutions mindful. Instead, English is the official language of Pakistan since independence. Of course, there is difference of opinion on the matter of Official Language. Among others, the medium of education and legislation including policy papers are the major issues which are on English pattern. However, if these issues are addressed, the constitutional obligation of implementing Urdu as official language may be ensured.

Challenges and Opportunities of English and Urdu contest: Language Planning and the Implementation of Article 251 in Pakistan

Immediately after independence in 1947, Urdu became the national language of Pakistan. The constitution of 1973 promised the realization of this goal in Article 251. This paper analyses the causes of its lack of implementation with the help of textual analysis of archival sources. A historical overview of the introduction and domination of the English language in South Asia through colonial machinery explains the reasons for ambivalence about English and Urdu language to be entrenched in the colonial legacy and anticolonial nationalism. The lack of capacity and will combines with the forces for globalism in enhancing the position of the English language in Pakistan. Further, the association of English and Urdu with the existing class division has reduced the debate about language policy to rhetorical postures. The paper proposes a reconsideration of the historical top-down formation of language policy and planning and its replacement with a renewed bottom-up approach where diversity in Pakistan is accessed as a resource instead of treating it as a problem.

Linguistic Landscape in Promotion of Language Through Traffic Signboards: An Introduction to the Signs in Pakistani Roads and Highways

This present research is about how the linguistic landscape brings about the promotion of language and public awareness. The linguistic landscape is a sociolinguistic phenomenon that is used for the promotion of language and culture as well. Linguistic landscape can be seen everywhere in the society as in advertisement billboards, traffic signboards, public awareness messages on the signboards, buildings, shopping centers, airports, etc. This study covers the dimension of traffic signboards and how they are consciously or unconsciously are promoting the language in Pakistan, especially the Urdu, the National language of Pakistan, English, the international language, the Mandarin, the language of China, and the Sign language. Two hundred and ten traffic signboards are selected for the completion of this research. The data collected from the traffic signboards of the motorways, highways of all the provinces of Pakistan. The purposive sampling technique is used for this study. This study will disclose how traffic signboards are promoting the language written over them, whether written or sing language. The data was analyzed through observation with the pictures of traffic signboards. The present study is qualitative in nature, so the data is analyzed and interpreted in the description form.

Smart Learning Companion (SLAC)

Augmented Reality (AR) tends to merge the computing world with the real world, giving way to an incredible user experience. This field is not only limited to entertainment but has been utilized in various domains including healthcare, education and training. Realizing the potential of Augmented Reality in improving the learning experience, researchers have explored many ways of incorporating AR in the field education. Consequently, this research is focused on providing interactive and customized learning experience to book readers. We present a mobile application, Smart Learning Companion (SLAC), for physical books that provide a virtual content for a book. The virtual content include, 3D animations, Quizzes, explanation of content in native language and many other features. The virtual content is activated as soon as pages are scanned with a mobile phone or tablet. Smart Learning Companion explains animated educational content and provides an interactive user experience. The aim of SLAC is to encourage students to learn on their own by making books more interactive. Smart Learning Companion provides explanation in Urdu, solutions of exercises with animations, quizzes for each section, and overall result that shows the student progress. This will help to reduce the dependency of students on others for learning making them capable of self-learning. Smart Learning Companion applications are developed for four books to conduct the experiments. Experimental study is conducted to show the effectiveness of Smart Learning Companion application. The results show that our application helped students to understand the concepts more easily as explanation was provided in national language of Pakistan, that is, Urdu.

Analyzing The Effect Of English Subject Teaching On The English Writing Skills Of Secondary School Student

English is known as international language for communication. If we talk about Pakistan, English is used as an official language however, the national language of Pakistan is Urdu. In schools, English language is taught as a compulsory subject. It plays a very important role in developing communication skills among students. The present research is related to analyze the effect of English subject teaching on the writing skills of Secondary School Students in South Punjab. In this research 9th class government and private school were taken as study sample. A writing skill test that was related to English language conducted for collecting the data. After collection of data a comparison chart was made. In this chart difference between both school students was clearly visible. Conclusion was made on the basis of result and in the end, suggestions were given for improving the writing skills of students.