Conference Information

ICNLP 2026: International Conference on Natural Language Processing

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Submission Date:
2026-02-20 Extended
Notification Date:
2026-03-05
Conference Date:
2026-03-20
Location:
Xi an, China
Years:
8
Viewed: 23836   Tracked: 8   Attend: 4

Call For Papers

ICNLP 2026 (International Conference on Natural Language Processing) is an academic conference held in Xi an, China on 2026-03-20. The paper submission deadline is 2026-02-20 (extended). Acceptance notifications are sent on 2026-03-05.

Topics of ICNLP 2026 include, but are not limited to, the following: 1. Language Analysis and Representation: Phonetics, Phonology, and Morphology; Syntax, Semantics, Discourse, Pragmatics, Dialogue, and Lexicon; Word and Sentence Representation; word and sentence representation; POS Tagging; Parsing; Semantic Role Labelling; Word-Sense Disambiguation; Multiword Expressions; Anaphora Resolution; 2. Language Processing Models and Techniques: Complexity; Mathematical, Statistical, Machine Learning, and Deep Learning Models; Mathematical and Statistical Models; Machine Learning Models; Deep Learning Models ; 3. Language Resources and Tools: Language Resources and Corpora; Electronic Dictionaries, Terminologies, and Ontologies; Linked Data; 4. Multilingual and Cross-Lingual Processing: Multilingual NLP; Machine Translation, Translation Memory Systems, and Computer-Aided Translation Tools; Text Simplification and Readability Estimation; 5. Information Extraction and Retrieval: Knowledge Acquisition; Information Retrieval; Text Categorization; Information Extraction; Text Summarization; Terminology Extraction; Question Answering; Fact Checking; 6. Sentiment Analysis and Human Interaction:: Opinion Mining and Sentiment Analysis; Stance Recognition; Author Profiling; Dialogue Systems; Computer-Aided Language Learning; 7. Specialized NLP Applications: NLP for Biomedical Texts and Healthcare; NLP for the Semantic Web; 8. Special Session I topics: Multi-modal Information Processing: Recent advances and applications Single Modal Data Analysis; Acquisition Techniques for Multi-Modal Data; Multi-Modal Representation and Translation; Multi-Modal Alignment and Fusion ; Cross-Modal Retrieval, Co-Training, Transfer Learning, Few/Zero-Shot Learning; Applications of Multi-Modal Information Processing;
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