大葉大學圖書館 |

Semi-supervised dependency parsing[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.35
書名/作者:	Semi-supervised dependency parsing/ by Wenliang Chen, Min Zhang.
作者:	Chen, Wenliang.
其他作者:	Zhang, Min.
出版者:	Singapore : : Springer Singapore :, 2015.
面頁冊數:	viii, 144 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Natural language processing (Computer science)
標題:	Grammar, Comparative and general - Parsing.
標題:	Mathematical linguistics.
標題:	Dependency grammar.
標題:	Linguistics.
標題:	Computational Linguistics.
ISBN:	9789812875525 (electronic bk.)
ISBN:	9789812875518 (paper)
內容註:	1 Introduction -- 2 Dependency Parsing Models -- 3 Overview of Semi-supervised Dependency Parsing Approaches -- 4 Training with Auto-parsed Whole Trees -- 5 Training with Lexical Information -- 6 Training with Bilexical Dependencies -- 7 Training with Subtree Structures -- 8 Training with Dependency Language Models -- 9 Training with Meta Features -- 10 Closing Remarks.
摘要、提要註:	This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing approaches have been proposed in recent works which utilize different types of information gleaned from unlabeled data. The book offers readers a comprehensive introduction to these approaches, making it ideally suited as a textbook for advanced undergraduate and graduate students and researchers in the fields of syntactic parsing and natural language processing.
電子資源:	http://dx.doi.org/10.1007/978-981-287-552-5

Semi-supervised dependency parsing[electronic resource] /
Chen, Wenliang.

Semi-supervised dependency parsing[electronic resource] /by Wenliang Chen, Min Zhang. - Singapore :Springer Singapore :2015. - viii, 144 p. :ill. (some col.), digital ;24 cm.

1 Introduction -- 2 Dependency Parsing Models -- 3 Overview of Semi-supervised Dependency Parsing Approaches -- 4 Training with Auto-parsed Whole Trees -- 5 Training with Lexical Information -- 6 Training with Bilexical Dependencies -- 7 Training with Subtree Structures -- 8 Training with Dependency Language Models -- 9 Training with Meta Features -- 10 Closing Remarks.

This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing approaches have been proposed in recent works which utilize different types of information gleaned from unlabeled data. The book offers readers a comprehensive introduction to these approaches, making it ideally suited as a textbook for advanced undergraduate and graduate students and researchers in the fields of syntactic parsing and natural language processing.

ISBN: 9789812875525 (electronic bk.)

Standard No.: 10.1007/978-981-287-552-5doiSubjects--Topical Terms:

411876
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Semi-supervised dependency parsing[electronic resource] /
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520 $a This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing approaches have been proposed in recent works which utilize different types of information gleaned from unlabeled data. The book offers readers a comprehensive introduction to these approaches, making it ideally suited as a textbook for advanced undergraduate and graduate students and researchers in the fields of syntactic parsing and natural language processing.
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