Semi-supervised dependency parsing [electronic resource] / Wenliang Chen, Min Zhang.
2015
QA76.9.N38
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Details
Title
Semi-supervised dependency parsing [electronic resource] / Wenliang Chen, Min Zhang.
Author
Chen, Wenliang, author.
ISBN
9789812875525 electronic book
9812875522 electronic book
9789812875518
9812875522 electronic book
9789812875518
Published
Singapore : Springer, 2015.
Language
English
Description
1 online resource (viii, 144 pages) : illustrations.
Call Number
QA76.9.N38
Dewey Decimal Classification
006.3/5
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed July 21, 2015).
Added Author
Zhang, Min, author.
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Table of Contents
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.
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.