Conference Information
CLNLP 2021: International Conference on Computational Linguistics and Natural Language Processing
http://www.clnlp.org/
Submission Date:
2021-05-31 Extended
Notification Date:
2021-05-31
Conference Date:
2021-07-23
Location:
Dalian, China
Years:
2
Viewed: 3450   Tracked: 0   Attend: 0

Conference Location
Call For Papers
★2021年计算语言学和自然语言处理国际会议(CLNLP 2021)---Ei Compendex&Scopus-Call for papers
|2021年7月23-25日,中国大连|网址: www.clnlp.org 

★CLNLP 2021将围绕“计算语言学和自然语言处理”的最新研究领域而展开,来自世界各地的主要研究人员和行业专家将通过论文和口头报告介绍最新研究成果。会议的这三天里,您将有机会聆听到前沿的学术报告,见证该领域的成果与进步。

★出版和索引
所有接受的论文将在线出版,将被Ei Compendex,SCOPUS,Google Scholar,Cambridge Scientific Abstracts(CSA),Inspec, ISTP等检索,优秀论文将在国际期刊上发表。

★特邀演讲嘉宾
一.Osvaldo N. Oliveira Jr教授——圣保罗大学,巴西
Osvaldo N.Oliveira Jr.是巴西圣保罗大学圣卡洛斯物理研究所的教授。他于2019年获得圣保罗大学理学学士和理学硕士学位、威尔士大学班戈分校博士学位(1990年)和南马托格罗索联邦大学荣誉博士学位(荣誉博士学位)。Oliveira教授是拉丁美洲科学院的成员,巴西材料研究协会的前主席,以及ACS应用材料和界面的执行主编。他领导了用Langmuir-Blodgett和自组装技术制备超薄薄膜的新材料的研究。这方面的工作大多与分子控制的超薄薄膜的基本特性有关,但技术方面也在具体项目中得到了解决。电子舌头就是这样,它对许多味觉的反应比人类的味觉系统敏感得多。近年来,奥利维拉教授率先将不同科学领域的方法结合使用,将统计物理和计算机科学的方法结合起来处理文本,并使用信息可视化来提高传感和生物传感的性能。这项开创性的工作将纳米技术与大数据分析和机器学习相结合,必将带来计算机辅助诊断系统等技术的发展。奥利维拉教授还制定了科学写作策略,特别是针对非英语母语使用者。截至2020年10月,他在国际期刊上发表了580多篇论文,出版了3本书,此外还提交了近12项专利,这些专利获得了约13700次引用(h=55,网络科学)和20500次引用(h=67,Google Scholar)。他在2006年被爱思唯尔授予斯科普斯奖,是巴西最有生产力的科学家之一。

★日程预览/日程一览
7月23日:注册+接待
7月24日:开幕式+ KN演讲+分会场报告
7月25日:分会场报告+实验室参观/半日游

★论文提交及要求
1.通过CMT投稿:https://cmt3.research.microsoft.com/CLNLP2021
2.投稿邮箱:clnlp@hksra.org 
3.投稿要求:
(1)稿件必须为全英文稿件,图片、表格、公式中均不允许有中文出现,稿件需与主题相关。
(2)主题突出,内容层次分明,数据准确可靠,论述严谨,结论明确。未在国内外公开刊物或其它学术会议上发表过。
(3)请作者按照官网上模板的格式编排(最好是在模板的基础上替换原文内容)。本次会议采取先投稿、先送审、符合条件者先发送录用通知方式进行。审稿周期约为3-7个工作日。
(4)投稿的文章经过初审之后,将提交给至少2名审稿委员会成员进行同行审查,审查内容包括原创性、技术或研究的内容和深度、会议主题相关性、贡献性和可读性。
(5)如果只参会做报告,不出版,则只需提交摘要以供审阅。听众不需要提交稿件,注册成功的听众可以参加会议的所有分会。
(6)稿件不允许有剽窃行为,涉嫌抄袭的文章将不会送审。

★联系我们
王女士
电子邮件:clnlp@hksra.org
网址:www.clnlp.org
QQ: 2011307354(加QQ时请附上您想咨询的会议名字简称,如:CLNLP 2021)

We invite submissions on a wide range of research topics, spanning both theoretical and systems research. The topics of interest include, but are not limited to:

Chunking
Cognitive and Psychological linguistics
Computational grammar
Computational models of partiality, underspecification, and context-dependency
Computational morphology
Computational neuroscience of language
Computational phonology
Computational pragmatics
Computational semantics
Computational semantics of natural languages
Computational syntax-semantics interface
Data science in language processing
Diagrammatic methods
Dialogue and Interactive Systems
Discourse and Pragmatics
Discourse processing
Document Analysis
Generation
Graphical methods
Information about space and time in language models and processing
Information extraction and database linking
Information Extraction and Text Mining
Information retrieval
Integration of formal methods
Interdisciplinary methods
Interfaces between morphology, lexicon, speech, text and pragmatics
Language learning
Language processing based on biological fundamentals of information and languages
Large-scale grammars of natural languages
Linguistic Theories, Cognitive Modeling and Psycholinguistics
Logic for information extraction or expression in written and spoken language
Machine learning of language
Machine Translation
Model theoretic methods
Models of computation and algorithms for linguistics
Models of computation and algorithms for natural language processing
Models of situations, contexts, and agents, for applications to language processing
Multi-lingual systems
Multidisciplinary
Multilingual processing
Multilinguality
Named entity recognition
Natural language generation
NLP tools/resources
Opinion mining and sentiment analysis
Phonology, Morphology and Word Segmentation
POS tagging
Question Answering
Resources and Evaluation
Semantic processing
Sentence-level semantics
Sentiment Analysis and Argument Mining
Social Media
Statistical methods
Summarization
Tagging, Chunking, Syntax and Parsing
Textual Entailment
Textual Inference and Other Areas of Semantics
Type theories for applications to language and information processing
Vision, Robotics, Multimodal, Grounding and Speech
Word segmentation
Word-level Semantics
Last updated by Zoe Wong in 2020-12-03
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