5^th International Conference on NLP, Data Mining and Machine Learning (NLDML 2026)

January 27 ~ 28, 2026, Virtual Conference

Registered authors are now able to present their work through our online platforms

Scope & Topics

5^th International Conference on NLP, Data Mining and Machine Learning (NLDML 2026) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.

The conference welcomes high quality contributions that advance theoretical foundations, propose novel algorithms, introduce innovative models, or demonstrate practical solutions to complex problems. By bringing together diverse perspectives from academia and industry, NLDML 2026 aims to foster collaboration, inspire new ideas, and accelerate progress across all major areas of Natural Language Computing, Data Mining, and Machine Learning.

Topics of interest include, but are not limited to, the following

Natural Language Processing


• Large Language Models (LLMs): Training, Evaluation, Alignment
  
• Retrieval Augmented Generation (RAG) and Knowledge Enhanced NLP
  
• Text Generation, Summarization, and Paraphrasing
  
• Information Extraction, Entity Linking, and Relation Extraction
  
• Semantic Processing, Semantic Parsing, and Representation Learning
  
• Question Answering, Reasoning, and Knowledge Intensive NLP
  
• Dialogue Systems, Conversational AI, and Intelligent Agents
  
• Argumentation Mining and Computational Social Science
  
• Low Resource, Cross Lingual, and Multilingual NLP
  
• NLP for Social Media, Misinformation, and Online Safety
  
  Multimodal, Speech and Cross Modal AI
  
  
• Vision Language, Speech Language, and Video Language Models
  
• Multimodal Fusion, Alignment, and Representation Learning
  
• Audio Processing, Speech Recognition, and Spoken Dialogue Systems
  
• Multisensory and Cross Modal Learning
  
  Machine Learning Foundations
  
  
• Deep Learning Architectures and Optimization
  
• Self Supervised, Semi Supervised, and Unsupervised Learning
  
• Reinforcement Learning and Decision Making Systems
  
• Meta Learning, Continual Learning, and Lifelong Learning
  
• Probabilistic Modeling and Bayesian Deep Learning
  
• Causal Machine Learning and Counterfactual Reasoning
  
• Federated, Distributed, and Privacy Preserving ML
  
• AutoML, Model Compression, and Efficient Inference
  
  Graph Machine Learning and Knowledge Enhanced AI
  
  
• Graph Neural Networks (GNNs) and Graph Mining
  
• Knowledge Graph Construction, Completion, and Reasoning
  
• Semantic Reasoning, Linked Data Integration, and Ontology Driven AI
  
• Hybrid Neural Symbolic Models and Structured Reasoning
  
  Data Centric AI and Scalable ML Systems
  
  
• Data Centric AI, Data Quality, and Data Governance
  
• Scalable ML Pipelines, Distributed Systems, and Big Data Processing
  
• Data Mining for Structured, Unstructured, and Streaming Data
  
• High Performance Computing for ML and Large Scale Model Training
  
  Trustworthy, Safe and Responsible AI
  
  
• Fairness, Accountability, Transparency, and Ethics in AI
  
• Adversarial ML, Robustness, and Secure Learning
  
• Bias Detection, Mitigation, and Responsible Dataset Design
  
• Explainable and Interpretable Machine Learning
  
• AI Safety, Red Teaming, and Risk Assessment
  
  Applied NLP and Domain Specific AI
  
  
• Healthcare, BioNLP, and Clinical Data Mining
  
• Finance, Economics, and Risk Modeling
  
• Legal NLP, Policy Analysis, and Government Applications
  
• Environmental AI, Climate Informatics, and Sustainability
  
• Education, Learning Analytics, and Intelligent Tutoring Systems
  
• Industrial AI, Smart Manufacturing, and Predictive Maintenance
  
• AI for Scientific Discovery and Automated Research
  
  Dataset Creation, Benchmarking and Evaluation Science
  
  
• Dataset Creation, Curation, and Annotation Methodologies
  
• Benchmark Design, Evaluation Frameworks, and Error Analysis
  
• Reproducibility, Model Diagnostics, and Evaluation Science
  
• Human in the Loop Evaluation and Annotation Quality
  

Paper Submission

Authors are invited to submit papers through the conference Submission System by January 17, 2026(Final Call). Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by International Journal on Cybernetics & Informatics (IJCI) (Confirmed).

Selected papers from NLDML 2026, after further revisions, will be published in the special issue of the following journals.

• International Journal on Natural Language Computing (IJNLC)
  
• International Journal of Web & Semantic Technology (IJWesT)
  
• International Journal of Ubiquitous Computing (IJU)
  
• International Journal of Data Mining & Knowledge Management Process (IJDKP)
  
• International Journal of Ambient Systems and Applications (IJASA)
  
• Machine Learning and Applications: An International Journal (MLAIJ)
  
• International Journal on Computational Science & Applications (IJCSA)
  
• Advances in Vision Computing: An International Journal (AVC)
  

Important Dates

Co - Located Event

• 5^th International Conference on Computing and Information Technology Trends (CCITT 2026)
  
• 5^th International Conference on Automation and Engineering (AUEN 2026)
  
• 5^th International Conference on Education and Technology (EDUTEC 2026)
  

***** The invited talk proposals can be submitted to nldml@nldml2026.org