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 machine learning and statistical techniques, deeper and more sophisticated models of language and speech are needed to make significant progress in both existing and newly emerging areas of computational language analysis. 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.



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

Table of Contents


Cleo Condoravdi, Valeria de Paiva, Annie Zaenen
The BIUTEE Research Platform for Transformation-based Textual Entailment Recognition PDF
Asher Stern, Ido Dagan
Is there a place for logic in recognizing textual entailment? PDF
Johan Bos
Nlog-like Inference and Commonsense Reasoning PDF
Lenhart Schubert
Decomposing Semantic Inferences PDF
Elena Cabrio, Bernardo Magnini
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
Synthetic Logic PDF
Alex J. Djalali
Recent Progress on Monotonicity PDF
Thomas Icard III, Lawrence Moss
The Relational Syllogism Revisted PDF
Ian Pratt-Hartmann
Intensions as Computable Functions PDF
Shalom Lappin
Frege in Space: A Program of Compositional Distributional Semantics PDF
Marco Baroni, Raffaela Bernardi, Roberto Zamparelli

ISSN: 1945-3604