Difference between revisions of "CoNLL 2020"
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|City=Punta Cana | |City=Punta Cana | ||
|Country=Dominican Republic | |Country=Dominican Republic | ||
+ | |has Keynote speaker=Emmanuel Dupoux, Kristina Tautanova | ||
}} | }} | ||
The '''24th SIGNLL Conference on Computational Natural Language Learning (CoNLL 2020)'''. SIGNLL (pronounce as signal) is the Special Interest Group on Natural Language Learning of the Association for Computational Linguistics (ACL). | The '''24th SIGNLL Conference on Computational Natural Language Learning (CoNLL 2020)'''. SIGNLL (pronounce as signal) is the Special Interest Group on Natural Language Learning of the Association for Computational Linguistics (ACL). | ||
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==Topics== | ==Topics== | ||
+ | *Computational models of human language acquisition | ||
+ | *Computational models of the origins and evolution of language | ||
+ | *Machine learning methods applied to natural language processing tasks (speech processing, phonology, morphology, syntax, semantics, discourse processing, *language engineering applications) | ||
+ | *Symbolic learning methods (Rule Induction and Decision Tree Learning, Lazy Learning, Inductive Logic Programming, Analytical Learning, Transformation- based Error-driven Learning) | ||
+ | *Biologically-inspired methods (Neural Networks, Evolutionary Computing) | ||
+ | *Statistical methods (Bayesian Learning, HMM, maximum entropy, SNoW, Support Vector Machines) | ||
+ | *Reinforcement Learning | ||
+ | Active learning, ensemble methods, meta-learning | ||
+ | *Computational Learning Theory analyses of language learning | ||
+ | *Empirical and theoretical comparisons of language learning methods | ||
+ | *Models of induction and analogy in Linguistics | ||
+ | |||
+ | |||
+ | |||
==Submissions== | ==Submissions== | ||
+ | Submitted papers must be anonymous and adhere to the CoNLL format by | ||
+ | using our LaTeX style files or Word template. Submitted papers may | ||
+ | consist of up to 8 pages of content plus unlimited space for | ||
+ | references. Authors of accepted papers will have an additional page | ||
+ | to address reviewers' comments in the camera-ready version (9 pages | ||
+ | of content in total, excluding references). Anonymized supplementary | ||
+ | materials are allowed as an optional PDF appendix, in line with | ||
+ | EMNLP 2020 guidelines. Submission is electronic, using the Softconf | ||
+ | START conference management system. | ||
+ | |||
+ | CoNLL adheres to the ACL anonymity policy, as described in the | ||
+ | EMNLP 2020 Call for Papers. | ||
+ | |||
+ | |||
==Important Dates== | ==Important Dates== | ||
Revision as of 10:17, 13 March 2020
CoNLL 2020 | |
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Conference on Natural Language Learning
| |
Event in series | CoNLL |
Dates | 2020/11/11 (iCal) - 2020/11/12 |
Homepage: | https://www.conll.org/2020 |
Location | |
Location: | Punta Cana, Dominican Republic |
Loading map... | |
Keynote speaker: | Emmanuel Dupoux, Kristina Tautanova |
Table of Contents | |
The 24th SIGNLL Conference on Computational Natural Language Learning (CoNLL 2020). SIGNLL (pronounce as signal) is the Special Interest Group on Natural Language Learning of the Association for Computational Linguistics (ACL).
Topics
- Computational models of human language acquisition
- Computational models of the origins and evolution of language
- Machine learning methods applied to natural language processing tasks (speech processing, phonology, morphology, syntax, semantics, discourse processing, *language engineering applications)
*Symbolic learning methods (Rule Induction and Decision Tree Learning, Lazy Learning, Inductive Logic Programming, Analytical Learning, Transformation- based Error-driven Learning) *Biologically-inspired methods (Neural Networks, Evolutionary Computing) *Statistical methods (Bayesian Learning, HMM, maximum entropy, SNoW, Support Vector Machines) *Reinforcement Learning Active learning, ensemble methods, meta-learning
- Computational Learning Theory analyses of language learning
- Empirical and theoretical comparisons of language learning methods
- Models of induction and analogy in Linguistics
Submissions
Submitted papers must be anonymous and adhere to the CoNLL format by using our LaTeX style files or Word template. Submitted papers may consist of up to 8 pages of content plus unlimited space for references. Authors of accepted papers will have an additional page to address reviewers' comments in the camera-ready version (9 pages of content in total, excluding references). Anonymized supplementary materials are allowed as an optional PDF appendix, in line with EMNLP 2020 guidelines. Submission is electronic, using the Softconf START conference management system.
CoNLL adheres to the ACL anonymity policy, as described in the EMNLP 2020 Call for Papers.
Important Dates
Committees
- Co-Organizers
- General Co-Chairs
- some person, some affiliation, country
- PC Co-Chairs
- some person, some affiliation, country
- Workshop Chair
- some person, some affiliation, country
- Panel Chair
- some person, some affiliation, country
- Seminars Chair
- some person, some affiliation, country
- Demonstration Co-Chairs
- some person, some affiliation, country
- some person, some affiliation, country
- Local Organizing Co-Chairs
- some person, some affiliation, country
- Program Committee Members
- some person, some affiliation, country
Acronym | CoNLL 2020 + |
End date | November 12, 2020 + |
Event in series | CoNLL + |
Event type | Conference + |
Has Keynote speaker | Emmanuel Dupoux + and Kristina Tautanova + |
Has OC member | Some person + |
Has PC member | Some person + |
Has coordinates | 18° 33' 24", -68° 22' 9"Latitude: 18.55655 Longitude: -68.369161111111 + |
Has demo chair | Some person + |
Has general chair | Some person + |
Has local chair | Some person + |
Has location city | Punta Cana + |
Has location country | Category:Dominican Republic + |
Has program chair | Some person + |
Has tutorial chair | Some person + |
Has workshop chair | Some person + |
Homepage | https://www.conll.org/2020 + |
IsA | Event + |
Start date | November 11, 2020 + |
Title | Conference on Natural Language Learning + |