Cover of: Connectionist, statistical, and symbolic approaches to learning for natural language processing | Read Online
Share

Connectionist, statistical, and symbolic approaches to learning for natural language processing

  • 286 Want to read
  • ·
  • 84 Currently reading

Published by Springer in Berlin, New York .
Written in English

Subjects:

  • Natural language processing (Computer science)

Book details:

Edition Notes

Includes bibliographical references.

StatementStefan Wermter, Ellen Riloff, Gabriele Scheler, (eds.).
SeriesLecture notes in computer science ;, 1040., Lecture notes in artificial intelligence, Lecture notes in computer science ;, 1040., Lecture notes in computer science.
ContributionsRiloff, Ellen., Scheler, Gabriele.
Classifications
LC ClassificationsQA76.9.N38 C67 1996
The Physical Object
Paginationix, 468 p :
Number of Pages468
ID Numbers
Open LibraryOL970221M
ISBN 103540609253
LC Control Number96006925

Download Connectionist, statistical, and symbolic approaches to learning for natural language processing

PDF EPUB FB2 MOBI RTF

This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August Most of the 32 papers included in the book are revised selected. Get this from a library! Connectionist, statistical, and symbolic approaches to learning for natural language processing. [Ellen Riloff; Gabriele Scheler;] -- "This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in. The book should bridge a gap between several areas that are usually discussed separately, including connectionist, statistical, and symbolic methods. In order to bring together new and different language learning approaches, we held a workshop at the International Joint Conference on Artificial Intelligence in Montreal in August The purpose of this chapter is to provide an introduction to the field of connectionist, statistical and symbolic approaches to learning for natural language processing, based on the contributions.

The purpose of this book is to present a collection of papers that represents a broad spectrum of current research in learning methods for natural language processing and to advance the state of the art in language learning and artificial. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The purpose of this chapter is to provide an introduction to the field of connectionist, statistical and symbolic approaches to learning for natural language processing, based on the contributions in this book. The introduction has been split into three parts: (1) neural networks and connectionist approaches, (2.   Cardie C. () Embedded machine learning systems for natural language processing: A general framework. In: Wermter S., Riloff E., Scheler G. (eds) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. IJCAI Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol Buy Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing by Stefan Wermter, Ellen Riloff from Waterstones today! Click and Collect from your local Waterstones or get FREE UK delivery on orders over £

  Connectionist Approach: The connectionist approach to natural language processing is a combination of the symbolic and statistical approaches. This approach starts with generally accepted rules of language and tailors them to specific applications from input derived from statistical inference. Connectionist, statistical and symbolic approaches to learning for natural language processing.   Also quite old, this book offers a unified vision of speech and language processing covering statistical and symbolic approaches to language processing. Abstract. The purpose of this chapter is to provide an introduction to the field of connectionist, statistical and symbolic approaches to learning for natural language processing, based on the contributions in this by: