Linguistic Issues in Language Technology ─ Perspectives on Semantic Representations for Textual Inference

Linguistic Issues in Language Technology (LiLT) is an open-access journal that focuses on the relationships between linguistic insights and language technology. 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 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. Taking an eclectic view on methodology, LiLT provides a forum for this work. In this volume, contributors offer new perspectives on semantic representations for textual inference.

Cleo Condoravdi is professor of linguistics at Stanford University. Valeria de Paiva is a mathematician and computer scientist at the Natural Language and AI Research Laboratory of Nuance Communications, Inc.Annie Zaenen is consulting professor in linguistics at Stanford University.