Relevant bibliographies by topics / Urdu language materials

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Urdu language materials'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Urdu language materials"

1

Shaukat, Saima, Muhammad Asad, and Asmara Akram. "Developing an Urdu Lemmatizer Using a Dictionary-Based Lookup Approach." Applied Sciences 13, no. 8 (April 19, 2023): 5103. http://dx.doi.org/10.3390/app13085103.

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Lemmatization aims at returning the root form of a word. The lemmatizer is envisioned as a vital instrument that can assist in many Natural Language Processing (NLP) tasks. These tasks include Information Retrieval, Word Sense Disambiguation, Machine Translation, Text Reuse, and Plagiarism Detection. Previous studies in the literature have focused on developing lemmatizers using rule-based approaches for English and other highly-resourced languages. However, there have been no thorough efforts for the development of a lemmatizer for most South Asian languages, specifically Urdu. Urdu is a morphologically rich language with many inflectional and derivational forms. This makes the development of an efficient Urdu lemmatizer a challenging task. A standardized lemmatizer would contribute towards establishing much-needed methodological resources for this low-resourced language, which are required to boost the performance of many Urdu NLP applications. This paper presents a lemmatization system for the Urdu language, based on a novel dictionary lookup approach. The contributions made through this research are the following: (1) the development of a large benchmark corpus for the Urdu language, (2) the exploration of the relationship between parts of speech tags and the lemmatizer, and (3) the development of standard approaches for an Urdu lemmatizer. Furthermore, we experimented with the impact of Part of Speech (PoS) on our proposed dictionary lookup approach. The empirical results showed that we achieved the best accuracy score of 76.44% through the proposed dictionary lookup approach.
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Akhter, Muhammad Pervez, Zheng Jiangbin, Irfan Raza Naqvi, Mohammed Abdelmajeed, and Muhammad Tariq Sadiq. "Automatic Detection of Offensive Language for Urdu and Roman Urdu." IEEE Access 8 (2020): 91213–26. http://dx.doi.org/10.1109/access.2020.2994950.

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Retta, Ephrem Afele, Richard Sutcliffe, Jabar Mahmood, Michael Abebe Berwo, Eiad Almekhlafi, Sajjad Ahmad Khan, Shehzad Ashraf Chaudhry, Mustafa Mhamed, and Jun Feng. "Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages." Applied Sciences 13, no. 23 (November 22, 2023): 12587. http://dx.doi.org/10.3390/app132312587.

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In a conventional speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language do not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German, and Urdu. For Amharic, we use our own publicly available Amharic Speech Emotion Dataset (ASED). For English, German and Urdu, we use the existing RAVDESS, EMO-DB, and URDU datasets. We followed previous research in mapping labels for all of the datasets to just two classes: positive and negative. Thus, we can compare performance on different languages directly and combine languages for training and testing. In Experiment 1, monolingual SER trials were carried out using three classifiers, AlexNet, VGGE (a proposed variant of VGG), and ResNet50. The results, averaged for the three models, were very similar for ASED and RAVDESS, suggesting that Amharic and English SER are equally difficult. Similarly, German SER is more difficult, and Urdu SER is easier. In Experiment 2, we trained on one language and tested on another, in both directions for each of the following pairs: Amharic↔German, Amharic↔English, and Amharic↔Urdu. The results with Amharic as the target suggested that using English or German as the source gives the best result. In Experiment 3, we trained on several non-Amharic languages and then tested on Amharic. The best accuracy obtained was several percentage points greater than the best accuracy in Experiment 2, suggesting that a better result can be obtained when using two or three non-Amharic languages for training than when using just one non-Amharic language. Overall, the results suggest that cross-lingual and multilingual training can be an effective strategy for training an SER classifier when resources for a language are scarce.
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JAFAR, MUHAMMAD RAHIM. "Impact of different factors affecting Urdu teaching in Pakistan: A Confirmatory Factor." Journal of Social Sciences and Economics 2, no. 1 (April 13, 2023): 26–34. http://dx.doi.org/10.61363/jsse.v2i1.69.

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In Pakistani society, Urdu is widely recognized as the official language of the country as well as one of the widely spoken languages. Urdu is regarded as one of the symbols of national and cultural identity and plays an important role in social life. This study used CFA to examine whether three factors in Urdu teaching (curriculum and teaching materials, teachers and teaching methods, and learning environment) had a significant impact on Urdu teaching. Confirmatory Factor Analysis (CFA) is a multivariate statistical method designed to verify whether a set of potential factors proposed by researchers can explain the variation in observed variables. CFA was used in this study to build a three-factor model that includes three factors: curriculum and teaching materials, teachers and teaching methods, and learning environment. The study will use sample data to perform parameter estimates of the model and use model fit metrics (fit index and standardized residuals) to assess how well the model fits.
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Akram, Dr Muhammad, and Dr Ayesha Qurrat ul Ain. "The Impact of the Partition of India on the Study of Hinduism in the Urdu Language." ĪQĀN 2, no. 04 (June 30, 2020): 69–86. http://dx.doi.org/10.36755/iqan.v2i04.147.

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Religion, language, and race have been among the most crucial factors behind the formation of various national and communal identities in modern South Asian history. Just like the political division of British India, the complex interplay of these factors also culminated in a bifurcation of linguistic boundaries along the religious lines according to which Urdu became associated with Islam and Muslims. In contrast, Hindi became increasingly connected to the Hindu culture. These historical developments also affected the extent and nature of the academic materials on Hinduism in the Urdu language, which the present paper examines. The paper takes stock of different relevant materials. Then, it discusses how the changed socio-political realities quantitatively and qualitatively affected the works on Hinduism in the Urdu language as the majority of the Hindu scholars lost enthusiasm to write on their religion in Urdu considering its increased perception of being a Muslim language. Muslims in Pakistan, on the other hand, lost opportunities of everyday interaction with Hindus and easy access to the original Hindi and Sanskrit sources resulting in a considerable decline in Hindu studies on their part. Thus, the overall production of literature on Hinduism in the Urdu language declined sharply. By implication, the paper hints at how decisively socio-political and historical contexts bear on the pursuit of the academic study of religion.
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Kottacheruvu, Nagendra. "Developing Writing Skills through English Short Stories: A Case Study in the Classroom." International Journal of Language and Literary Studies 5, no. 1 (April 3, 2023): 287–99. http://dx.doi.org/10.36892/ijlls.v5i1.1243.

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This study seeks to determine the efficacy and utility of teaching writing skills through short stories to third-semester postgraduate students whose native language and language of instruction is Urdu at Maulana Azad National Urdu University in Hyderabad, India. This article investigates primarily the extent to which selected English short stories are useful and effective for teaching English writing skills. Due to their Urdu-influenced origins, it is a fact that the majority of our students struggle to speak and write in English. Due to their socio-cultural, fiscal, and pedagogical circumstances, as well as the way they are taught, they rarely interact with the target language. They are always hesitant to speak and write in the target language due to fear, hesitancy, and lack of experience, despite their passion for learning English. Mastering writing is regarded as one of the most transferable skills because it requires an extensive vocabulary and correct grammar. In addition, it requires constant effort, practise, reading, and exposure to language use. Students from both Madarasa and Urdu backgrounds with little exposure and practise find mastering writing to be the most challenging endeavour. The findings of this study indicate that reading English short stories improves writing skills.
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Khan, Wahab, Ali Daud, Khurram Shahzad, Tehmina Amjad, Ameen Banjar, and Heba Fasihuddin. "Urdu Named Entity Recognition Using Conditional Random Fields." Applied Sciences 12, no. 13 (June 23, 2022): 6391. http://dx.doi.org/10.3390/app12136391.

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Named entity recognition (NER) is an important task in natural language processing, as it is widely featured as a key information extraction sub-task with numerous application areas. A plethora of attempts was made for NER detection in Western and Asian languages. However, little effort has been made to develop techniques for the Urdu language, which is a prominent South Asian language with hundreds of millions of speakers across the globe. NER in Urdu is considered a hard problem owing to several reasons, including the paucity of large, annotated datasets; an inaccurate tokenizer; and the absence of capitalization in the Urdu language. To this end, this study proposed a conditional-random-field-based technique with both language-dependent and language-independent features, such as part-of-speech tags and context windows of words, respectively. As a second contribution, we developed an Urdu NER dataset (UNER-I) in which a large number of NE types were manually annotated. To evaluate the effectiveness of the proposed approach, as well as the usefulness of the dataset, experiments were performed using the dataset we developed and an existing dataset. The results of the experiments showed that our proposed technique outperformed the baseline technique for both datasets by improving the F1 scores by 1.5% to 3%. Furthermore, the results demonstrated that the enhanced dataset was useful for learning and prediction in a supervised learning approach.
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Khan, Muzammil, Kifayat Ullah, Yasser Alharbi, Ali Alferaidi, Talal Saad Alharbi, Kusum Yadav, Naif Alsharabi, and Aakash Ahmad. "Understanding the Research Challenges in Low-Resource Language and Linking Bilingual News Articles in Multilingual News Archive." Applied Sciences 13, no. 15 (July 25, 2023): 8566. http://dx.doi.org/10.3390/app13158566.

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The developed world has focused on Web preservation compared to the developing world, especially news preservation for future generations. However, the news published online is volatile because of constant changes in the technologies used to disseminate information and the formats used for publication. News preservation became more complicated and challenging when the archive began to contain articles from low-resourced and morphologically complex languages like Urdu and Arabic, along with English news articles. The digital news story preservation framework is enriched with eighteen sources for Urdu, Arabic, and English news sources. This study presents challenges in low-resource languages (LRLs), research challenges, and details of how the framework is enhanced. In this paper, we introduce a multilingual news archive and discuss the digital news story extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, i.e., English, and low-resource languages, i.e., Urdu and Arabic. LRLs encountered a high error rate during preservation compared to high-resource languages (HRLs), corresponding to 10% and 03%, respectively. The extraction results show that few news sources are not regularly updated and release few new news stories online. LRLs require more detailed study for accurate news content extraction and archiving for future access. LRLs and HRLs enrich the digital news story preservation (DNSP) framework. The Digital News Stories Archive (DNSA) preserves a huge number of news articles from multiple news sources in LRLs and HRLs. This paper presents research challenges encountered during the preservation of Urdu and Arabic-language news articles to create a multilingual news archive. The second part of the paper compares two bilingual linking mechanisms for Urdu-to-English-language news articles in the DNSA: the common ratio measure for dual language (CRMDL) and the similarity measure based on transliteration words (SMTW) with the cosine similarity measure (CSM) baseline technique. The experimental results show that the SMTW is more effective than the CRMDL and CSM for linking Urdu-to-English news articles. The precision improved from 46% and 50% to 60%, and the recall improved from 64% and 67% to 82% for CSM, CRMDL, and SMTW, respectively, with improved impact of common terms as well.
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Aiman, Aisha, Yao Shen, Malika Bendechache, Irum Inayat, and Teerath Kumar. "AUDD: Audio Urdu Digits Dataset for Automatic Audio Urdu Digit Recognition." Applied Sciences 11, no. 19 (September 23, 2021): 8842. http://dx.doi.org/10.3390/app11198842.

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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.
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10

Afzal, Nimra, Shoaib Waqas, Muhammad Tariq, Asifa Javaid, and Hafiz Muhammad Asim. "Translation and Validation Scale for Assessment and Rating of Ataxia in Urdu Language for Cerebral Palsy Patients." Pakistan Journal of Medical and Health Sciences 16, no. 9 (September 30, 2022): 58–59. http://dx.doi.org/10.53350/pjmhs2216958.

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Aim: To translate the Scale for assessment and rating of Ataxia from English to Urdu language Methods: The study employed a linguistic validation study design with a non-random sample approach and was done at different clinical setups of Lahore. Parents of children with ataxic cerebral palsy with children aged 2 to 15 years old who speak Urdu should contact us. Sara's English version was translated into Urdu by two translators, one with a medical background and the other with a technical background in Urdu, both proficient in Urdu and English. Two individuals with medical and technology backgrounds who are competent in Urdu to English translation reverse translated the translated versions of SARA-Urdu I and SARA-Urdu II into English. The re-translated versions of SARA-Urdu I and SARA-Urdu II were translated back into English (SARA English III and SARA English IV). The translated versions were compared and generated a new Urdu version SARA Urdu-V. The data was entered into the SPSS version 23 application, which was also used to analyze it. Results: The translated version of the SARA scale has a Cronbach's alpha of 0.883. The inter-item correlation between the total SARA score and the eight domains of gait, stance, sitting, speech disturbance, finger chase, nose finger test, fast alternating hand movements, and heel shin slide was 0.131, 0.046, 0.159, 0.188, 0.136, 0.400, 0.698, and 0.450, respectively, after the translation of the Urdu version of SARA. For test retest reliability, the Pearson correlation value varied from 0.400 to 0.842. Conclusion: The Urdu version of SARA has appropriate internal consistency and fair inter-item correlation, and hence may be utilized by Urdu speakers. Keywords: Cerebral palsy, Cerebellar ataxia, Scale for assessment and rating of Ataxia
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Dissertations / Theses on the topic "Urdu language materials"

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Abdullah, Sohail. "Hissār." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3145.

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Hissaar is a noun and a verb, it is the periphery and the extremities, and the walls and the fortress. And it is to encircle, to wrap and to contain. This paper is an inexhaustive account of thoughts, experiences and lessons learned, of varying forms that influence my aesthetic sensibilities, my art-value system, and my art- ethical concerns. They provide for my art the impetus for its perpetual (and perhaps circular) journey. It is about finding connections between the fraying ends of free floating ideas. The following fragments explores how words make ideas, ideas make images, images make memory; memory sets into architecture, architecture moves the body, the body needs pain and pain needs words.
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Books on the topic "Urdu language materials"

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McGregor, Ronald Stuart. Urdu study materials: For use with Outline of Hindi grammar. Delhi: Oxford University Press, 1992.

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Ṭhakkar, Anil. Pas-i ashk: Nāvil. Naʼī Dihlī: Mauḍarn Pablishing Hāʼūs, 2017.

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Ṣiddīqī, ʻĀmir Muṣt̤afá. Uskī k̲h̲ūshbū aks̲ar ātī he: Afsānaun̲ se afsāncaun̲ tak. Dihlī: Ejūkeshnal Pablishing Hāʼūs, 2016.

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K̲h̲ān, Muḥammad Ḥafīẓ. Anvāsī: Nāvil = Anwasi. Jihlam: Buk Kārnar, 2019.

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Malik, Afshān̲. Iẓt̤irāb: Afsāne. Dihlī: Ejūkeshnal Pablishing Hāʼūs, 2017.

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McGregor, R. S. Urdu study materials for use with outline of Hindi grammar. Delhi: Oxford University Press, 1992.

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Ṣiddīqī, Mubīn. Ījādāt: "Ḥāliyah" ek t̤arz bhī, ek ṣinf bhī. Dihlī: Ejūkeshnal Pablishing Hāʼūs, 2018.

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Molteno, Marion. Teaching Britain's community languages: Materials and methods : with examples from teaching Urdu. London: Centre for Information on Language Teaching and Research, 1986.

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Qadīr, Ashhar. Abūlʻamal Ḥakīm ʻAbdulḥamīd. Naʼī Dihlī: Shuʻbah-yi Kulliyāt, Jāmiʻah Hamdard, 2017.

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Velthuijs, Max. Bretkocka eshte bretkocke =: Frog is frog. London, England: Milet, 2000.

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Book chapters on the topic "Urdu language materials"

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Christine, C. "Introduction to This Volume." In The Literature of Lashkar-e-Tayyaba, 1–18. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780198883937.003.0001.

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Abstract This chapter provides a brief background to this volume, which offers English translations of a curated sample of Urdu-language foundational texts from the vast body of literature produced and disseminated across Pakistan and beyond by a Pakistan-based international terrorist organization known as Lashkar-e-Tayyaba (LeT) through their in-house Lahore-based publisher, Dar-ul-Andlus. It introduces the Lashkar-e-Tayyaba and the various front organizations it has spawned. In examining the tensions between Pakistan and India, it discusses the influences of the United States and its interests on their relations. It describes the methodology used to collect and curate materials for translation and provides an overview of each chapter of the volume.
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Conference papers on the topic "Urdu language materials"

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Mukhammad, Khalid Innaiat Ali, and Irina Mikhailovna Timofeeva. "Reliance on invariant grammatical content when teaching the Russian language to foreign students." In International Research-to-practice conference. Publishing house Sreda, 2019. http://dx.doi.org/10.31483/r-33230.

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Annotation The article is devoted to the pedagogical strategy of modeling the educational process of foreign students based on the invariant content of contacting languages: studied - Russian and native - Urdu, taking into account the structures of the intermediate language (English). As the material of the study, the grammatical category of the verb type included in the standard text is considered, producing certain invariant meanings (invariant semantic content). The aim of the work is to model the pedagogical strategy of teaching the Russian language to Pakistani and Indian students at level A0 - A2, based on universal semantic content. The novelty of the search is the creation of a nationally oriented pedagogical strategy based on invariant (universal) units of contact languages.
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