| LiLT | Linguistic Issues in Language Technology |
| Editors | |
| Annie Zaenen, PARC Inc. |
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| Bonnie Webber, University of Edinburgh, UK |
|
| Martha Palmer, University of Colorado, USA |
|
| Editorial Board |
|
| Jason Baldridge, UT, Austin, USA |
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| Johan Bos, University of Rome, Italy |
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| Gosse Bouma, University of Groningen, The Netherlands |
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| John Carroll, University of Sussex, UK |
|
| Robin Cooper, Göteborg University, Sweden |
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| Ann Copestake, University of Cambridge, UK |
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| Robert Dale, Macquarie University, Australia |
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| Jason Eisner, Johns Hopkins University, USA |
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| Elisabet Engdahl, Göteborg University, Sweden |
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| Dan Flickinger, Stanford University, USA |
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| Anette Frank, University of Heidelberg, Germany |
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| John Goldsmith, The University of Chicago, USA |
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| Mary Harper, University of Maryland, USA |
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| Chu-Ren Huang, Academia Sinica, Taiwan |
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| Mark Johnson, Brown University, USA |
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| Aravind Joshi, UPenn, USA |
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| Ron Kaplan, PowerSet, USA |
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| Martin Kay, Stanford University, USA |
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| Lori Lamel, LIMSI, France |
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| Roger Levy, UCSD, USA |
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| Mark Liberman, UPenn, USA |
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| Christopher Manning, Stanford University, USA |
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| Detmar Meurers, The Ohio State University, USA |
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| Gertjan van Noord, University of Groningen, The Netherlands |
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| Kemal Oflazer, Sabancı University, Turkey |
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| Janet Pierrehumbert, Northwestern University, USA |
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| Livia Polanyi, PowerSet, USA |
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| Stephen Pulman, Oxford University, UK |
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| Philip Resnik, University of Maryland, USA |
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| Hinrich Schütze, University of Stuttgart, Germany |
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| Mark Steedman, University of Edinburgh, UK |
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| Matthew Stone, Rutgers University, USA |
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| Jun’ichi Tsujii, University of Tokyo, Japan |
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Linguistic Issues in Language Technology (LiLT) is a new open-access journal that focusses on relationships between linguistic insights, which can prove valuable to language technology, and language technology, which can enrich linguistic research.
The Editorial Board of LiLT believes that, in conjunction with machine learning and statistical techniques, deeper and more sophisticated models of language and speech are needed to make significant progress in newly emerging areas of computational language analysis. LiLT provides a forum for such work. LiLT takes an eclectic view on methodology.
Submissions should be sent electronically to
Annie Zaenen
and follow the LiLT style sheet (see LiLT Style)
Final submissions should be
submitted in PDF format.
LiLT accepts short research notes (< 4 pages), squibs (<8 pages) and
full fledged articles (no page limit). We will review research notes
within 3-4 weeks, squibs within 6 weeks and full fledged papers within
2 months. Articles will be published as soon as they are in their
final state. The on-line version is free of charge. Once a year the
contributions will be gathered in a printed volume.
Issues that fall in the purview of LiLT include but are not limited to:
automatic recognition of entailment; reference resolution procedures;
anaphora resolution; formalisms for linguistic context, ellipsis, and
coordination in dialogue that interface with implemented approaches to
conversational reasoning; parsing models that quantify syntactic and semantic
relations; machine-readable resources including lexicons, thesauri and
ontologies; annotation schemas for syntactic, semantic and pragmatic
phenomena in diverse languages and genres; linguistically informed techniques
for error-analysis and performance evaluation; typologically motivated
linguistic generalizations and contrastive studies that might inform cross
linguistic applications and machine translation; high-level representations
for language modeling in ASR; semantic role labeling; building semantic
representations; incorporating linguistic information into statistical
language models; grammar engineering; applications of formal mathematical
models to language technology; use of linguistic information for
bootstrapping data collections; use of statistics in rule-based linguistic
models; use of linguistic insights in statistical and machine learning
modeling of natural language.