会议信息

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

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NLPIR
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截稿日期:
2026-10-10 还有 19 天 Extended
通知日期:
2026-10-30
会议日期:
2026-12-11
会议地点:
Nara, Japan
届数:
10
主办方:
浏览: 22759   关注: 7   参加: 3

会伴指数 (CP-I)

49.6 / 100
全站第 1,662 名 / 共 5,682 个会议 · 前 30%

自然语言处理与语音 第 30 / 85 数据挖掘与数据库 第 89 / 337

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
65
社区关注 (10%)
40
资料公开度 (15%)
35

用到的输入: 有据可查的届次：10 · 在会伴关注它的研究者：7 人 · 过去 24 个月打开过本页的研究者：13 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-20

征稿

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 2026-10-10 (extended). Acceptance notifications are sent on 2026-10-30.

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
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