NLPIR 2026 (International Conference on Natural Language Processing and Information Retrieval) is an academic conference held in Nara, Japan on 2026-12-11. The paper submission deadline is 2026-06-30. Acceptance notifications are sent on 2026-07-30.
NLPIR is one of the key academic conferences to present research results and new developments in the area of the Natural Language Processing and Information Retrieval. For its 10th edition, NLPIR 2026 will be held in Nara, Japan during December 11-13, 2026.
The topics of interests for submission include, but are not limited to:
Core NLP & Data Science
Foundations of Language Processing
•Data/text mining, corpus linguistics, and psycholinguistic modeling
•Basic NLP pipelines: tokenization, POS tagging, lemmatization, dependency parsing, and semantic role labeling
•Low-resource language engineering and cross-lingual adaptation
Linguistic Analysis & Understanding
•Syntax, semantics, discourse analysis, and pragmatics
•Multimodal speech recognition/synthesis (ASR/TTS) and conversational AI
•Diachronic corpora, temporal reasoning, and evolving language models
Knowledge Systems & Semantics
•Automated knowledge acquisition, ontology generation/alignment, and semantic web technologies
•Neuro-symbolic integration: combining logic-based reasoning with neural networks
Content Analysis & IR
•Topic modeling, event/anomaly detection, and sentiment/emotion analysis
•Document summarization, plagiarism detection, and authorship attribution
•Dynamic/personalized IR, adversarial retrieval, and cross-language systems
Social & Multimedia Analysis
•Personality/emotion detection in social media, misinformation tracking
•Multimodal IR (text, image, video) and virality prediction
AI-Driven Methods & Innovations
Large Language Models (LLMs) & Transformers
•Architectures (BERT, GPT, T5, LLaMA) for NLU, generation, and few-shot learning
•Domain-specific LLMs (e.g., BioGPT, Codex) and tools like ChatGPT, DeepSeek, Claude
•Ethical challenges: bias mitigation, hallucination control, and AI-generated content detection
Generative AI & Automation
•Abstractive summarization, synthetic data generation, and conversational agents
•Multimodal LLMs (e.g., GPT-4V) for vision-language tasks
Graph & Deep Learning
•GNNs for co-occurrence graphs, knowledge graph completion, and dynamic networks
•Swarm intelligence hybridized with transformer architectures
Efficiency & Scalability
•Model compression (pruning, quantization), federated learning, and edge NLP
•Distributed training frameworks for trillion-parameter models
Cross-Cutting Themes
Human-Centric NLP
•Interactive AI: chatbots, dynamic query resolution, and personalized recommendation systems
•Explainability (XAI) and visualization of attention mechanisms
Machine Translation & Multilinguality
•Zero-shot translation, LLM-driven low-resource adaptation, and post-editing workflows
Decentralized & Collaborative Systems
•Blockchain for decentralized knowledge graphs, federated search, and privacy-preserving NLP
Ethics & Governance
•AI safety, fairness audits, and regulatory compliance (e.g., EU AI Act)
•Combatting misinformation and deepfakes in social/content platforms
Emerging Frontiers
AI for Science: LLMs in biomedical NLP, climate text analysis, and legal document processing
Embodied AI: Language models integrated with robotics and real-world interaction
Self-Supervised Learning: Pre-training paradigms beyond transformers