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Semi-Supervised Dependency Parsing

  • Wenliang Chen
  • Min Zhang

Table of contents

  1. Front Matter
    Pages i-viii
  2. Wenliang Chen, Min Zhang
    Pages 1-10
  3. Wenliang Chen, Min Zhang
    Pages 11-25
  4. Wenliang Chen, Min Zhang
    Pages 27-31
  5. Wenliang Chen, Min Zhang
    Pages 33-47
  6. Wenliang Chen, Min Zhang
    Pages 49-56
  7. Wenliang Chen, Min Zhang
    Pages 57-70
  8. Wenliang Chen, Min Zhang
    Pages 71-105
  9. Wenliang Chen, Min Zhang
    Pages 107-126
  10. Wenliang Chen, Min Zhang
    Pages 127-140
  11. Wenliang Chen, Min Zhang
    Pages 141-144

About this book

Introduction

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.

Keywords

Big Data for Parsing Dependency parsing Dependency trees Natural language processing Parsing performance Partial Tree Structures Semi-supervised Learning Semi-supervised dependency parsing Syntactic Parsing

Authors and affiliations

  • Wenliang Chen
    • 1
  • Min Zhang
    • 2
  1. 1.Soochow UniversitySuzhouChina
  2. 2.Soochow UniversitySuzhouChina

Bibliographic information