会议信息

CoNLL 2025: The SIGNLL Conference on Computational Natural Language Learning

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截稿日期:
2025-03-14
通知日期:
2025-05-23
会议日期:
2025-07-31
会议地点:
Vienna, Austria
届数:
29
CCF: C   ICORE: B   QUALIS: B1   浏览: 100498   关注: 102   参加: 13

征稿

CoNLL 2025 (The SIGNLL Conference on Computational Natural Language Learning) is a CCF C / ICORE B / QUALIS B1 conference held in Vienna, Austria on 2025-07-31. The paper submission deadline is 2025-03-14. Acceptance notifications are sent on 2025-05-23.

SIGNLL invites submissions to the 29th Conference on Computational Natural Language Learning (CoNLL 2025). The focus of CoNLL is on theoretically, cognitively and scientifically motivated approaches to computational linguistics. We welcome work targeting any aspect of language and its computational modeling, including but not only: Computational Psycholinguistics, Cognition and Linguistics Computational Social Science and Sociolinguistics Interaction and Dialogue Language Acquisition, Learning, Emergence, and Evolution Multimodality and Grounding Typology and Multilinguality Speech and Phonology Syntax and Morphology Lexical, Compositional and Discourse Semantics Theoretical Analysis and Interpretation of ML Models for NLP Resources and Tools for Scientifically Motivated Researcha
由 Dou Sun 最后更新于

录用率

平均录用率: 30.6% 14 年间 (2003–2018).

年份提交数录用数录用率(%)
20182745720.8%
20172715018.5%
20161493020.1%
20151273829.9%
2014832024.1%
20131002525%
2011782734.6%
2010812530.9%
2009592542.4%
2008852023.5%
2006521834.6%
2005701927.1%
2004231147.8%
2003351748.6%

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相关期刊

CCF全称影响因子出版商ISSN
CNatural Computing1.6Springer1567-7818
CKnowledge-Based Systems7.2Elsevier0950-7051
CFuture Generation Computer Systems6.1Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
CIEEE Transactions on Industrial Informatics11.7IEEE1551-3203
CIEEE Internet of Things Journal8.9IEEE2327-4662
CEngineering Applications of Artificial Intelligence8.0Elsevier0952-1976
CExpert Systems with Applications7.5Elsevier0957-4174
CIEEE Transactions on Big Data5.7IEEE2332-7790

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