A Computational Approach to Understanding Agglutinative Structures in Urdu
This study investigates the computational challenges and opportunities presented by the agglutinative structures in Urdu, a language characterized by its complex system of morpheme-based word formation. Agglutinative languages, including Urdu, pose significant...
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Corpus Research Center
2024-09-01
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| Series: | Corporum |
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| Online Access: | https://journals.au.edu.pk/ojscrc/index.php/crc/article/view/309/180 |
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| author | Muhammad Shoaib Tahir Mahnoor Amjad |
| author_facet | Muhammad Shoaib Tahir Mahnoor Amjad |
| author_sort | Muhammad Shoaib Tahir |
| collection | DOAJ |
| description | This study investigates the computational challenges and opportunities presented by the agglutinative structures in Urdu, a language characterized by its complex system of morpheme-based word formation. Agglutinative languages, including Urdu, pose significant difficulties in natural language processing (NLP) due to the intricate ways in which morphemes each carrying distinct grammatical or semantic meanings are combined to form words. Despite its linguistic richness and central role among South Asian languages, Urdu has been relatively underrepresented in global computational research, leading to a lack of robust NLP tools tailored to its unique morphological features. This gap highlights the need for extensive linguistic resources, including annotated corpora and models that can specifically address the complexities of Urdu's agglutinative morphology, which remain largely unexplored. Using the Emille Urdu Corpus, this research systematically analyzes the frequency and distribution of agglutinative structures in Urdu. A Python-based annotation process was employed to tag prefixes and suffixes, facilitating a more granular understanding of Urdu morphology. The study highlights key patterns, such as the prevalent use of prefixes like "نا-" (nā-) and "بد-" (bad-) to form words with negative connotations and the transformation of adjectives and verbs into nouns through suffixes like "-گی " (gī) and "-ی" (ī). Furthermore, the research explores the limitations of traditional rule-based models in handling Urdu’s morphological complexity and advocates for the adoption of machine learning and deep learning techniques. These modern approaches, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), show promise in accurately modeling Urdu's agglutinative morphology, though they require extensive linguistic data and computational resources. The findings underscore the need for comprehensive linguistic resources and advanced computational models to enhance Urdu NLP. By addressing these challenges, the study aims to contribute to the development of more effective and scalable NLP tools, thereby improving access to Urdu-language content in digital platforms and advancing the broader field of computational linguistics for agglutinative languages. |
| format | Article |
| id | doaj-art-ebb572c5eaf84550bdd2917e82c12ed8 |
| institution | Kabale University |
| issn | 2617-2917 2707-787X |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Corpus Research Center |
| record_format | Article |
| series | Corporum |
| spelling | doaj-art-ebb572c5eaf84550bdd2917e82c12ed82025-01-28T15:28:08ZengCorpus Research CenterCorporum2617-29172707-787X2024-09-01715678A Computational Approach to Understanding Agglutinative Structures in UrduMuhammad Shoaib Tahir0Mahnoor Amjad1Visiting Lecturer, Government College University, FaisalabadVisiting Lecturer, University of OkaraThis study investigates the computational challenges and opportunities presented by the agglutinative structures in Urdu, a language characterized by its complex system of morpheme-based word formation. Agglutinative languages, including Urdu, pose significant difficulties in natural language processing (NLP) due to the intricate ways in which morphemes each carrying distinct grammatical or semantic meanings are combined to form words. Despite its linguistic richness and central role among South Asian languages, Urdu has been relatively underrepresented in global computational research, leading to a lack of robust NLP tools tailored to its unique morphological features. This gap highlights the need for extensive linguistic resources, including annotated corpora and models that can specifically address the complexities of Urdu's agglutinative morphology, which remain largely unexplored. Using the Emille Urdu Corpus, this research systematically analyzes the frequency and distribution of agglutinative structures in Urdu. A Python-based annotation process was employed to tag prefixes and suffixes, facilitating a more granular understanding of Urdu morphology. The study highlights key patterns, such as the prevalent use of prefixes like "نا-" (nā-) and "بد-" (bad-) to form words with negative connotations and the transformation of adjectives and verbs into nouns through suffixes like "-گی " (gī) and "-ی" (ī). Furthermore, the research explores the limitations of traditional rule-based models in handling Urdu’s morphological complexity and advocates for the adoption of machine learning and deep learning techniques. These modern approaches, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), show promise in accurately modeling Urdu's agglutinative morphology, though they require extensive linguistic data and computational resources. The findings underscore the need for comprehensive linguistic resources and advanced computational models to enhance Urdu NLP. By addressing these challenges, the study aims to contribute to the development of more effective and scalable NLP tools, thereby improving access to Urdu-language content in digital platforms and advancing the broader field of computational linguistics for agglutinative languages.https://journals.au.edu.pk/ojscrc/index.php/crc/article/view/309/180agglutinativecomputationalnatural language processingurdu |
| spellingShingle | Muhammad Shoaib Tahir Mahnoor Amjad A Computational Approach to Understanding Agglutinative Structures in Urdu Corporum agglutinative computational natural language processing urdu |
| title | A Computational Approach to Understanding Agglutinative Structures in Urdu |
| title_full | A Computational Approach to Understanding Agglutinative Structures in Urdu |
| title_fullStr | A Computational Approach to Understanding Agglutinative Structures in Urdu |
| title_full_unstemmed | A Computational Approach to Understanding Agglutinative Structures in Urdu |
| title_short | A Computational Approach to Understanding Agglutinative Structures in Urdu |
| title_sort | computational approach to understanding agglutinative structures in urdu |
| topic | agglutinative computational natural language processing urdu |
| url | https://journals.au.edu.pk/ojscrc/index.php/crc/article/view/309/180 |
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