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
|Towards a Semantic Model for Textual Entailment Annotation
Assaf Toledo, Stavroula Alexandropoupou, Sophie Chesney, Sophia Katrenko, Heidi Klockmann, Pepijn Kokke, Benno Kruit, Yoad Winter