Special call on "Language Equity Through NLP: Addressing Low-Resource Challenges" in the Computación y Sistemas (CyS)
Organization: National Institute of Technology Silchar
This special call on "Language Equity Through NLP: Addressing Low-Resource Challenges" in the Computación y Sistemas (CyS) aims to serve as a comprehensive resource for researchers, and other stakeholders who are committed to advancing Natural Language Processing (NLP) for low-resource languages.
Announcement: https://www.cys. cic.ipn.mx/ojs/index.php/CyS/ announcement
Submission Guidelines
The special issue will explore a wide range of topics, including but not limited to:
Data collection and preprocessing techniques for low-resource languages
Feature extraction and modeling strategies to overcome resource constraints
Evaluation frameworks designed for low-resource NLP applications
Cross-lingual transfer learning and zero-shot techniques.Ethical considerations in NLP for low-resource communities
Quantum NLP for low-resource communities.
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
Full papers: 30 pages
Submission Guidelines and Deadlines:
Proposal Submission Deadline: April 30, 2025
First Review/Desk Rejection: May 30, 2025
Second Review: June 30, 2025
Final Acceptance: July 30, 2025
Final Online Publication: September 2025
Submissions should be made through the CyS journal portal: Submission Link under "Thematic Section for Low Resource Languages", Submission Link: https://www.cys.cic.ipn. mx/ojs/index.php/CyS/login
List of Topics:
Low Resources Natural Language Processing
Quantum Natural Language Processing
Machine Translation
Question Answering
Language Model
ChatBot
Sentiment Analysis
Text Similarity
Multilingual and Cross-lingual Models
Speech and Multimodal NLP
Data Augmentation Techniques
Few-shot and Zero-shot Learning
Named Entity Recognition (NER) and POS Tagging
Resource Creation and Benchmarking
Code-Switching and Mixed-Language NLP
Evaluation and Fairness in Multilingual Models
Language Documentation with NLP
Guest Editors:
Dr. Partha Pakray, National Institute of Technology, Silchar, India (partha@cse.nits.ac.in)
Dr. Sudip Kumar Naskar, Jadavpur University, Kolkata, India (sudip.naskar@gmail.com)
Prof. Sivaji Bandyopadhyay, Jadavpur University, Kolkata, India
Contact:
Dr. Partha Pakray, National Institute of Technology, Silchar, India (partha@cse.nits.ac.in)
Partha Pakray