NLCS 2013 opening slides
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This document discusses the history and current state of studying logic, computation, and language. It notes several European and North American conferences on these topics that have been held since the 1980s. It also mentions related areas of study like formal grammar, programming languages, and language technology. While statistical natural language processing is developing rapidly and creating jobs, the document raises some concerns about overreliance on machine learning and a lack of experiments hybridizing different methods. It concludes by asking for thoughts on pulling resources together or creating a new workshop or mailing list.
![AN ILLUSTRIOUS HERITAGE…
of studying Logic, Computation and Language in parallel:
ESSLLI European Summer School on Logic, Language and Information -- since 1989
NASSLLI North American ESSLLI -- for 6th edition in 2014
LACL Logical Aspects of Computational Linguistics, 7th edition 2012, since 1995
WOLLIC Workshop on Logic, Language Information and Computation since 1994
MOL Mathematics of Language
Formal Grammar
[ LLC, CTCS ] etc…](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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NLCS 2013 opening slides
- 2. AN ILLUSTRIOUS HERITAGE… of studying Logic, Computation and Language in parallel: ESSLLI European Summer School on Logic, Language and Information -- since 1989 NASSLLI North American ESSLLI -- for 6th edition in 2014 LACL Logical Aspects of Computational Linguistics, 7th edition 2012, since 1995 WOLLIC Workshop on Logic, Language Information and Computation since 1994 MOL Mathematics of Language Formal Grammar [ LLC, CTCS ] etc…
- 3. METHODS AND TOOLS FROM CS/ PROGRAMMING LANGUAGES… Proof theoretical semantics Type theoretical analysis Implicit Complexity Analysis Games Semantics Coalgebraic/categorical methods
- 4. LANGUAGE TECHNOLOGY… For language, number of bigger meetings ACL NAACL COLING LREC EMNLP Etc.. Plus Automated Deduction/AI/KR ones
- 5. A GOOD TIME? Statistical NLP developing at speed More jobs, internships, projects, open source software tools than ever… Siri, Watson good PR for language BUT issues too: Too much derivative machine learning Too little learning from machine learning Too much reliance on ‘made-up’ numbers Not enough experiments hybridizing methods
- 6. TWO GRAPHS: A NOTE OF CAUTION? From: Studying the History of Ideas Using Topic Models, by Hall, Juravsky and Manning 2008,
- 7. THOUGHTS, SUGGESTIONS? Pulling resources? New workshop? Backburner? A new mailing list? Get in touch! Thanks!
- 8. PROGRAM 9 - 10 AM Wlodek Zadrozny 10 - 10:20 Break 10:20 Robin Cooper 11:20 Bruno Mery 11:55 Christophe Fouquere 12:35 - 2pm Lunch 2:00 - 3:00 Ian Pratt-Hartmann 3:00 - 3:40 Alex Djalali 3:40 Break 4:00 - 4:40 Wren Thornton 4:40 - 5:30 Larry Moss