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

January 27 ~ 28, 2026, Virtual Conference

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