Conference Paper2019Cited by 3

An Approach of Handling Verbal Inflections of Bengali Text: Conversion of Sadhu to Cholito Form of Language

Mohammad Masudur Rahman, Ashraful Islam, Md. Faisal Kabir, Mohammad Nurul Huda

IEEE 22nd International Conference on Computer and Information Technology (ICCIT 2019)

IEEE, pp. 1-5

DOI: 10.1109/iccit48885.2019.9038570URL

Abstract

Bengali language follows Subject-Object-Verb (SOV) structure for constructing sentences where verb plays a crucial role. Machine translation for Bengali becomes easier if the verb is identified correctly in a semantically valid sentence. However, Bengali verbs are affected by various complex inflections while using in a sentence. Those inflections are determined according to tense (time of occurring event), person (nature of the subject), Sadhu and Cholito forms (historical evolution of writing styles of Bengali) of language. In this study, we propose an algorithm to identify different inflections of Bengali verbs to retrieve verb roots and present an approach to convert Sadhu to Cholito form of writing Bengali sentences. The methodology shows satisfying accuracy and fluency in the context of language evaluation.

Keywords

  • verb inflection
  • historical writing style

CCDS Authors

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

MD Faisal Kabir, PhD
Md Faisal Kabir, PhD

MD Faisal Kabir, PhD

  • Collaborator, HCI Wing

Assistant Professor

Computer Science

Pennsylvania State University Harrisburg

Research Interests

Data Mining, Machine Learning, Deep Learning, Health Informatics, Information and Communication Technology (ICT) for health research

References

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  2. 2.Suniti Kumar Chatterji. (2024). The Origin and Development of the Bengali Language. [10.4324/9781003481188]
  3. 3.Mohammad Masudur Rahman, Md Faisal Kabir, Mohammad Nurul Huda. (2018). A Corpus Based N-gram Hybrid Approach of Bengali to English Machine Translation. , 1–6[10.1109/iccitechn.2018.8631938]
  4. 4.Mozammel Haque, Mohammad Nurul Huda. (2016). Relation between subject and verb in Bangla Language: A semantic analysis. , 41–44[10.1109/iciev.2016.7760134]
  5. 5.Mozammel Haque, Mahmudul Hasan. (2018). English to Bengali Machine Translation: An Analysis of Semantically Appropriate Verbs. , 217–221[10.1109/iciset.2018.8745626]
  6. 6.Arijit Das, Tapas Halder, Diganta Saha. (2017). Automatic extraction of Bengali root verbs using Paninian grammar. , 11, 953–956[10.1109/rteict.2017.8256739]

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  2. 2.Samiul Karim Mazumder, Sabiha Jannat, Snaholata Mondal. (2025). Transformer-Based Approach for Accurately Identifying Saint and Common Forms of Bangla Language. , 432–437[10.1109/wiecon-ece69386.2025.11525929]
  3. 3.Jarin Nooder, Ashrarfi Mahbuba, Shayla Sharmin, Nazmun Nessa Moon, Lamisha Haque Poushy, Salauddin Ahmed Bhuiyan, Samia Nawshin. (2021). Machine Learning Approach to Find Students' Best Place to Study: Home vs Hostel. , 3, 123–129[10.1109/icitech50181.2021.9590130]
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