International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Active Learning in Natural Language Processing |
|---|---|
| ABSTRACT | Active Learning Techniques for Low-Resource Natural Language Processing. Low-resource languages and domains pose a major challenge for Natural Language Processing (NLP) due to the lack of labelled data. Active learning offers a practical solution by selecting the most informative samples for annotation, often with expert guidance. This review paper analyses 20-25 recent research papers that explore active learning strategies for low resource NLP tasks. It compares techniques, datasets, annotation efficiency, and expert-in-the-loop designs, offering a practical overview for students and researchers interested in building custom language models with minimal data. |
| AUTHOR | NILESH SAWARKAR Department of Technology, Savitribai Phule Pune University, Pune, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405090 |
| pdf/90_Active Learning in Natural Language Processing.pdf | |
| KEYWORDS | |
| References | [1] Settles, B. (2009). Active Learning Literature Survey. [2] Siddhant, A., & Lipton, Z. (2018). Deep Active Learning for Named Entity Recognition. [3] Nguyen, H., & Smeulders, A. (2004). Active Learning Using Pre-clustering. [4] Zhang, Y., et al. (2021). Active Learning for Multilingual Sentiment Analysis. [5] Peris, A., & Casacuberta, F. (2019). Active Learning for Neural Machine Translation. [6] Kreutzer, J., et al. (2020). Human-AI Collaboration in Translation. [7] Rijhwani, S., et al. (2020). Active Learning for Named Entity Recognition in Low-Resource Languages. [8] Goyal, P., et al. (2022). Active Learning for Indian Languages. [9] Bloodgood, M., & Callison-Burch, C. (2010). Using Mechanical Turk for NLP Annotation. [10] Yuan, X., et al. (2021). Simulated Annotation for Active Learning. |
