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NLPIR 2026: International Conference on Natural Language Processing and Information Retrieval

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Submission Date:
2026-09-10 Due in 18 days Extended
Notification Date:
2026-10-10
Conference Date:
2026-12-11
Location:
Nara, Japan
Years:
10
Organizer:
Viewed: 22193   Tracked: 7   Attend: 3

Call For Papers

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 2026-09-10 (extended). Acceptance notifications are sent on 2026-10-10.

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
Last updated by Dunn Carl on

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