{"id":258,"date":"2024-10-18T15:32:05","date_gmt":"2024-10-18T15:32:05","guid":{"rendered":"https:\/\/www.ctrans.in\/?page_id=258"},"modified":"2024-10-18T15:32:06","modified_gmt":"2024-10-18T15:32:06","slug":"centre-for-transdisciplinary-studies","status":"publish","type":"page","link":"https:\/\/www.ctrans.in\/","title":{"rendered":"Centre for Transdisciplinary Studies\u00a0"},"content":{"rendered":"\n

CTrans is a department at Dr. Bhimrao Ambedkar University, Agra that focuses on interdisciplinary education and research.<\/p>\n\n\n\n

The Centre for Transdisciplinary Studies (CTrans) offers various programs in linguistic, computational linguistic, and language technologies.<\/p>\n\n\n\n

The center is based in Agra where it runs various academic programs and workshops with the main aim of blending linguistic studies with computational methods.<\/p>\n\n\n\n

The center’s emphasis is mainly on the application of language technologies to socio-linguistic development.<\/p>\n\n\n\n

CTrans is one of the prestigious colleges for transdisciplinary and linguistic studies in India known for various awards.<\/p>\n\n\n\n

Courses Offered in Centre for Transdisciplinary Studies (CTrans)<\/strong><\/h2>\n\n\n\n

The Centre for Transdisciplinary Studies, Agra mainly offers two types of courses in the field of transdisciplinary and linguistic studies.<\/p>\n\n\n\n

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  1. NLP Courses<\/li>\n\n\n\n
  2. Linguistic Courses<\/li>\n<\/ol>\n\n\n\n

    NLP Courses<\/strong><\/h3>\n\n\n\n

    The Natural Language Processing (NLP) Course is an introductory course in different methods and approaches to the development of language resources with a focus on South Asian \/ Indian languages. <\/p>\n\n\n\n

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    1. Fundamentals of language technologies and computational linguistics<\/li>\n\n\n\n
    2. Different kinds of resources and their applications – text, speech, and multimodal<\/li>\n\n\n\n
    3. Data collection, Processing,\u00a0 and time-aligned transcription (of speech and multimodal data)<\/li>\n\n\n\n
    4. Pre-processing and preparing the text dataset \u2013 tokenization, stemming, lemmatization normalization, and data cleaning; Regular Expressions<\/li>\n\n\n\n
    5. Pre-processing and preparing the speech and multimodal datasets<\/li>\n\n\n\n
    6. Infrastructure and representation for language resources \u2013 Plain text, CSV, XML, JSON, etc.<\/li>\n\n\n\n
    7. XML, JSON, and other Web Technologies for data sharing and exchange<\/li>\n\n\n\n
    8. Semantic Web, Linked Open Data and SPARQL<\/li>\n\n\n\n
    9. N-gram Language Models \u2013 evaluation and smoothing techniques, Laplace, Add-K, Kenser-Ney, and Stupid Backoff Smoothing<\/li>\n\n\n\n
    10. Vector Semantics and Embeddings, (Multilingual) Language Models and Neural Language Models; Contextual Embeddings and Derivatives of BERT<\/li>\n\n\n\n
    11. Large Language Models – open-source models. Prompting and Instruct Tuning<\/li>\n\n\n\n
    12. Data Annotation – manual, semi-automatic and crowdsourced annotations<\/li>\n\n\n\n
    13. Evaluating a dataset; Inter-annotator agreement reliability and measures – Percentage, Cohen\u2019s Kappa, Fleiss Kappa, Krippendorff\u2019s Alpha, etc. Each of the above ideas will be discussed with respect to the three broad sources of data\n