Refine your search
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
, Diksha
- Research Publications on SARS-CoV-2 (COVID-19):A Study of Publication Trends using the R Package
Abstract Views :234 |
PDF Views:0
Authors
Diksha
1,
Rupak Chakravarty
2
Affiliations
1 Librarian, Divisional Library (South), Chandigarh, IN
2 Department of Library and Information Science, Panjab University, Chandigarh, IN
1 Librarian, Divisional Library (South), Chandigarh, IN
2 Department of Library and Information Science, Panjab University, Chandigarh, IN
Source
International Journal of Information Studies & Libraries, Vol 5, No 2 (2020), Pagination: 66-81Abstract
This study provides a bibliometric review of 1,027 documents published on COVID-19, extracted from the database of ‘dimensions.ai’ and published in 228 journals, authored by 3,436 authors. For the analysis, Bibliometrix R-Package was used through the biblioshiny interface. A topical query was conducted and 2,973 bibliographic literature from the online database of “Dimensions.ai” was downloaded using the search strategy “Text – ‘Coronavirus OR COVID-19 OR SARS-CoV-2’ in the title and abstract; Field of Research is Division code 08 Information and Computing Science”. Documents from the earliest possible record to the current record of Bibliometric study revealed a sudden rise in the annual scientific production in the year 2019 with the advent of the COVID-19 pandemic. Further study revealed the most prolific authors, journals, and affiliations. Besides, we present research collaboration networks at the author level.Keywords
COVID-19, SARS-CoV-2, Bibliometrix, R-Package, Dimensions.ai
- A Comparative study on the Impact of Online and Offline Classes among the nursing students in selected colleges of nursing at Distt. Mandi (H.P).
Abstract Views :137 |
PDF Views:0
Authors
Affiliations
1 Associate Professor, Abhilashi College of Nursing, Mandi. H P., IN
2 Associate Professor, Abhilashi College of Nursing, Mandi H.P., IN
3 B.Sc. Nursing students Abhilashi College of Nursing, Mandi H.P., IN
1 Associate Professor, Abhilashi College of Nursing, Mandi. H P., IN
2 Associate Professor, Abhilashi College of Nursing, Mandi H.P., IN
3 B.Sc. Nursing students Abhilashi College of Nursing, Mandi H.P., IN
Source
International Journal of Advances in Nursing Management, Vol 10, No 4 (2022), Pagination: 389-391Abstract
Education is a process of receiving or giving systematic instruction especially at schools or university. It is the process of facilitating learning or the acquisition of knowledge, skills, value moral belief and personal development. Researcher conducted “A comparative study on the impact of online and offline classes among nursing students in selected college of nursing at district Mandi [H.P]. A quantitative research approach and comparative design was used to assess the impact of online and offline class among the nursing students. Total samples of study were 200 nursing students and convenience sampling technique was used to select the sample. A self-structured questionnaire was prepared to assess the impact of online and offline classes among nursingstudents. The result of the study shows that 85% nursing students has average impact on online classes and 66% nursing student has good impact on offline classes. There was significant association between the score level and socio -demographic variables with type of previous medium. There is no significance association between the level of score and other demographic variables with any type of psychological health issue. The study concluded that online classes are better than offline classes.Keywords
Compare, impact, online and offline, nursing students.References
- Albert Einstein. Education quote. (internet). Cited on 30 March 2012 available from: https://blog.ampli.coalbert-einstein-quoteseducation- 15-of.html.
- Education definition.(internet).Cited on January 2021 available from : https://en.wikipedia.org/wiki/Education
- Sander Tamm. What is definition of e learning.(internet).cited on 21 December 2020 Available from: https://e-student.org/what-is-e- learning/
- Ranu Rawat, Parmal Singh impact of online and offline education on students during corona cited on 11 September 2020 Available from:http://www.njcmindia.org/home/article/9/11/2020/September
- Comparing State-of-the-art Models for Language Detection Methods on Short Texts from Twitter
Abstract Views :185 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science & Engineering Indira Gandhi Delhi Technical University for Women Delhi, India., IN
1 Department of Computer Science & Engineering Indira Gandhi Delhi Technical University for Women Delhi, India., IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35 (2023), Pagination: 01-13Abstract
Short text communication via microblogging platforms like Twitter has become the norm in today’s fast-paced world. These platforms have a global reach; thus, usage of multiple languages (including region-specific languages) is common. Language detection is an important task that finds its application in several NLP tasks as data is available for further analysis, only once its natural language has been detected. In our work, we have analysed and compared the performances of two major state-of-the-art models, which are Naive-Bayes and Logistic Regression for identification of the natural languages, on short-text data. Both the models were trained on a dataset from Kaggle, which made them capable of detecting 22 languages. They were compared on different parameters like accuracy, precision, recall, and f1 score, and it was learnt that Logistic Regression works better on relatively small datasets like ours.Keywords
Natural language Processing, Language Detection, Logistic Regression, Naive Bayes.Full Text
References
- Timothy Baldwin and Marco Lui. 2010. Language Identification: The Long and the Short of the Matter. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 229– 237, Los Angeles, California. Association for Computational Linguistics
- Gamallo, Pablo & Garcia, Marcos & Sotelo, Susana & Campos, José. (2014). Comparing ranking-based and Naive Bayes approaches to language detection on tweets. CEUR Workshop Proceedings. 1228. 12-16
- Sarthak, Shukla, S., Mittal, G. (2019). Spoken Language Identification Using ConvNets. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham.
- Konthala Yasaswini, Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Sajeetha Thavareesan, and Bharathi Raja Chakravarthi. 2021. IIITT@DravidianLangTechEACL2021: Transfer Learning for Offensive Language Detection in Dravidian Languages. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 187–194, Kyiv. Association for Computational Linguistics.
- Pidong Wang, Nikhil Bojja, and Shivasankari Kannan. 2015. A Language Detection System for Short Chats in Mobile Games. In Proceedings of the third International Workshop on Natural Language Processing for Social Media, pages 20–28, Denver, Colorado. Association for Computational Linguistics.
- Swanson, Ben & Charniak, Eugene. (2012). Native language detection with tree substitution grammars. Vol. 2. 193-197.
- Thoma, Martin. “The WiLI benchmark dataset for written language identification.” ArXiv abs/1801.07779 (2018): n. pag.
- Marco Lui, Jey Han Lau, and Timothy Baldwin. 2014. Automatic Detection and Language Identification of Multilingual Documents. Transactions of the Association for Computational Linguistics, 2:27–40.
- Jônatas Wehrmann, Willian E. Becker, and Rodrigo C. Barros. 2018. A multi-task neural network for multilingual sentiment classification and language detection on Twitter. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC '18). Association for Computing Machinery, New York, NY, USA, 1805–1812. https://doi.org/10.1145/3167132.3167325
- Wiegand, Michael & Ruppenhofer, Josef. (2021). Exploiting Emojis for Abusive Language Detection. 369-380. 10.18653/v1/2021.eacl-main.28.
- P. Chakraborty and M. H. Seddiqui, "Threat and Abusive Language Detection on Social Media in Bengali Language," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-6, doi: 10.1109/ICASERT.2019.8934609.
- Sai Muralidhar Jayanthi, Kavya Nerella, Khyathi Raghavi Chandu, and Alan W Black. 2021. CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic CodeSwitching, pages 113–118, Online. Association for Computational Linguistics.
- Bharathi B and Agnusimmaculate Silvia A. 2021. SSNCSE_NLP@DravidianLangTechEACL2021: Offensive Language Identification on Multilingual Code Mixing Text. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 313–318, Kyiv. Association for Computational Linguistics.
- Yash Kumar Lal, Vaibhav Kumar, Mrinal Dhar, Manish Shrivastava, and Philipp Koehn. 2019. De-Mixing Sentiment from Code-Mixed Text. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 371–377, Florence, Italy. Association for Computational Linguistics.
- Yili Ma, Liang Zhao, and Jie Hao. 2020. XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 975–980, Barcelona (online). International Committee for Computational Linguistics.
- Li, Shuyue Stella and Kenton Murray. “Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns.” ArXiv abs/2211.07628 (2022): n. Pag.
- Kodali, Prashant & Sachan, Tanmay & Goindani, Akshay & Goel, Anmol & Ahuja, Naman & Shrivastava, Manish & Kumaraguru, Ponnurangam. (2022). PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix Quality. 10.48550/arXiv.2206.07988.
- Prashant Kodali, Anmol Goel, Monojit Choudhury, Manish Shrivastava, and Ponnurangam Kumaraguru. 2022. SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing. In Findings of the Association for Computational Linguistics: ACL 2022, pages 472–480, Dublin, Ireland. Association for Computational Linguistics.
- SaiKrishna Rallabandi, Sunayana Sitaram, and Alan W Black. 2018. Automatic Detection of Code-switching Style from Acoustics. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 76–81, Melbourne, Australia. Association for Computational Linguistics.
- Aditya Shah and Chandresh Maurya. 2021. How effective is incongruity? Implications for code-mixed sarcasm detection. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 271–276, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
- Barman, Utsab & Das, Amitava & Wagner, Joachim & Foster, Jennifer. (2014). Code Mixing: A Challenge for Language Identification in the Language of Social Media. 0.13140/2.1.3385.6967.
- Winata, Genta & Cahyawijaya, Samuel & Liu, Zihan & Lin, Zhaojiang & Madotto, Andrea & Fung, Pascale. (2021). Are Multilingual Models Effective in Code-Switching?. 142-153. 10.18653/v1/2021.calcs-1.20.
- Chan, Joyce. (2006). Automatic speech recognition of Cantonese-English code-mixing utterances /.
- Jinhua Du, Yan Huang, and Karo Moilanen. 2019. AIG Investments.AI at the FinSBD Task: Sentence Boundary Detection through Sequence Labelling and BERT Fine-tuning. In Proceedings of the First
- J. Y. C. Chan, P. C. Ching, Tan Lee and H. M. Meng, "Detection of language boundary in code-switching utterances by bi-phone probabilities," 2004 International Symposium on Chinese Spoken Language Processing, Hong Kong, China, 2004, pp. 293-296, doi: 10.1109/CHINSL.2004.1409644.
- Balazevic, Ivana et al. “Language Detection For Short Text Messages In Social Media.” ArXiv abs/1608.08515 (2016): n. Pag.
- Shruti Rijhwani, Royal Sequiera, Monojit Choudhury, Kalika Bali, and Chandra Shekhar Maddila. 2017. Estimating Code-Switching on Twitter with a Novel Generalized WordLevel Language Detection Technique. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1971–1982, Vancouver, Canada. Association for Computational Linguistics.
- Suman Dowlagar and Radhika Mamidi. 2021. A Pre-trained Transformer and CNN Model with Joint Language ID and Part-of-Speech Tagging for Code-Mixed SocialMedia Text. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 367–374, Held Online. INCOMA Ltd.
- Santy, Sebastin, Anirudh, Srinivasan, and Monojit, Choudhury. "BERTologiCoMix: How does Code-Mixing interact with Multilingual BERT?."In AdaptNLP EACL 2021.
- https://www.kaggle.com/code/martinkk5575/language-detection/data (Kaggle dataset)
- https://www.kaggle.com/code/rtatman/analyzing-multilingual-data/data (Tweet data)
- https://www.kaggle.com/code/ayushmi77al/language-detection-nlp/notebook
- https://thecleverprogrammer.com/2021/10/30/language-detection-with-machine-learning/
- https://www.sciencedirect.com/