Linguistic Issues in Language Technology Vol 9

Annie Zaenen

ISBN 10: 157586844X / ISBN 13: 9781575868448

Published by Centre for the Study of Language and Information, US, 2015

About this Item

Linguistic Issues in Language Technology focuses on the relationships between linguistic insights and language technology. In conjunction with machine learning and statistical techniques, 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 provides unprecedented opportunities for data-intensive analysis of linguistic phenomena, which can in turn enrich computational methods. Linguistic Issues in Language Technology provides a forum for this work. In this volume, contributors offer new perspectives on semantic representations for textual inference.

Seller Inventory # LU-9781575868448

Bibliographic Details

• Title: Linguistic Issues in Language Technology Vol 9
• Publisher: Centre for the Study of Language and Information, US
• Publication Date: 2015
• Language: English
• ISBN 10: 157586844X
• ISBN 13: 9781575868448
• Binding: Paperback
• Condition: New
• Dimensions: 15.24W x 3.05H x 22.86D centimeters 15.24 centimeters width by 3.05 centimeters height by 22.86 centimeters depth
• Weight: 482 grams

About this title

Synopsis

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.

About the Authors

Annie Zaenen is consulting professor in linguistics at Stanford University.

 

 


Valeria de Paiva is senior research scientist at Nuance Communications and an honorary fellow in the School of Computer Science at the University of Birmingham, UK.