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Linguistic Issues in Language Technology Vol 9 : Perspectives on Semantic Representations for Textual Inference (Paperback)
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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.
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- Book formatPaperback
- Fiction/nonfictionNon-Fiction
- GenreNonfiction
- Publication dateJanuary, 2015
- Pages364
- Number in series9
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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.
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.
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Book format
Paperback
Fiction/nonfiction
Non-Fiction
Genre
Nonfiction
Publication date
January, 2015
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