Linguistic Issues in Language Technology

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 ( LiLT is hosted externally by CSLI Publications:

Vol 9 (2013): Perspectives on Semantic Representations for Textual Inference

Table of Contents


Intensions as Computable Functions PDF
Shalom Lappin
Synthetic Logic PDF
Alex J. Djalali
Is there a place for logic in recognizing textual entailment? PDF
Johan Bos
The BIUTEE Research Platform for Transformation-based Textual Entailment Recognition PDF
Asher Stern, Ido Dagan
Decomposing Semantic Inferences PDF
Elena Cabrio, Bernardo Magnini
Frege in Space: A Program of Compositional Distributional Semantics PDF
Marco Baroni, Raffaela Bernardi, Roberto Zamparelli
Recent Progress on Monotonicity PDF
Thomas Icard III, Lawrence Moss
Towards a Semantic Model for Textual Entailment Annotation PDF
Assaf Toledo, Stavroula Alexandropoupou, Sophie Chesney, Sophia Katrenko, Heidi Klockmann, Pepijn Kokke, Benno Kruit, Yoad Winter
Nlog-like Inference and Commonsense Reasoning PDF
Lenhart Schubert
The Relational Syllogism Revisted PDF
Ian Pratt-Hartmann

ISSN: 1945-3604