COLING 2020
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COLING 2020 | |
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International Conference on Computational Linguistics (COLING)
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Ordinal | 28 |
Event in series | COLING |
Dates | 2020/12/08 (iCal) - 2020/12/13 |
Presence | online |
Homepage: | https://coling2020.org/pages/call for papers |
Twitter account: | @coling2020 |
Submitting link: | https://coling2020.org/pages/call for papers |
Location | |
Location: | Barcelona, Online |
Important dates | |
Submissions: | 2020/07/01 |
Notification: | 2020/10/01 |
Camera ready due: | 2020/11/01 |
Subevents: MWE-LEX-2020
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Committees | |
General chairs: | Donia Scott |
PC chairs: | Nuria Bel, Chengqing Zong |
Workshop chairs: | Xiaodan Zhu, Preslav Nakov, Jun Zhao |
Keynote speaker: | Harald Baayen, Monica Lam, Tal Linzen, Margaret Mitchell, Amanda Stent |
Table of Contents | |
Tweets by @coling2020 | |
Topics
COLING’2020 invites the submission of long and short papers on original and unpublished research covering all aspects of natural language and computation. Relevant topics for the conference include, but are not limited to, the following (in alphabetical order):
- Applications (including BioNLP and eHealth, NLP for legal purposes, NLP for Social Media and Journalism, among others)
- Dialogue and Interactive Systems
- Discourse and Pragmatics
- Document classification, topic modelling, information retrieval and crosslingual retrieval
- Language Generation
- Language Modelling
- Information Extraction and Text Mining, and Knowledge Graph
- Language Resources and Evaluation
- CL/NLP and Linguistic Theories, Cognitive Modeling and Psycholinguistics
- Machine Learning for CL/NLP
- Multilingual processing, Machine Translation and translation aids
- Morphology and Word Segmentation
- Semantics (of words, sentences, ontologies and lexical semantics)
- Sentiment Analysis, Opinion and Argument Mining
- Speech recognition, text-to-speech and spoken language understanding
- Summarization and Simplification
- Tagging, Chunking, Syntax and Parsing
- Textual Inference and Question Answering
- Vision, Robotics, Multimodal and Grounded Language Acquisition