LiLT (Linguistic Issues in Language Technology)

Linguistic Issues in Language Technology (LiLT) is an 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 sophisticated computational methods, deeper and more sophisticated models of language and speech are needed to make significant progress in both existing and newly emerging areas of language processing. The Board also believes that the vast quantity of electronically-accessible natural language data (text and speech, annotated and unannotated, formal and informal) provides unprecedented opportunities for data-intensive analysis of linguistic phenomena, which can in turn enrich computational methods. LiLT provides a forum for such work. LiLT takes an eclectic view on methodology.

Volume 1 to 8 of the journal can be found at 

http://journals.linguisticsociety.org/elanguage/lilt/index.html


Announcements

 

SUBMISSIONS TO LILT

 

Please, send submissions directly to

azaenen@stanford.edu.

When we use the journal submission system some people take advantage of the access to vandalize the site (sigh).

 
Posted: 2018-11-05
 

LiLT at Stanford

 

LiLT is now hosted at Stanford. For older issues see:

http://journals.linguisticsociety.org/elanguage/lilt/issue/archive.html

 
Posted: 2014-09-09 More...
 
More Announcements...

Vol 18 (2019): Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing

The articles in this special issue are based on presentations made at the international symposium entitled Exploiting Parsed Cor- pora: Applications in Research, Pedagogy, and Processing held at the National Institute for Japanese Language and Linguistics (NINJAL) on Dec. 9-10, 2017 and organized by the collaborative research project at NINJAL entitled 'Development of and Linguistic Research with a Parsed Corpus of Japanese'.

Table of Contents

Editorial

 
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Articles

Yusuke Kubuta, Ai Kubuta
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Bin Li, Yuan Wen, Li Song, Weiguang Qu, Nianwen Xue
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Hideki Kishimoto, Prashant Pardeshi
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Susan Pintzuk
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Sean Wallis, Ian Cushing, Bas Aarts
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