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Semi-Supervised Dependency Parsing

  • Book
  • © 2015

Overview

Authors:
  1. Wenliang Chen

    1. Soochow University, Suzhou, China

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    You can also search for this author in PubMed   Google Scholar

  2. Min Zhang

    1. Soochow University, Suzhou, China

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    You can also search for this author in PubMed   Google Scholar

  • Presents a comprehensive overview of semi-supervised approaches to dependency parsing
  • Bridges the gap between small human-annotated training data and huge raw data for dependency parsing
  • Explains why semi-supervised approaches are well suited to dependency parsing
  • Clarifies the differences between the three levels of information for semi-supervised dependency parsing

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-viii
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  2. Introduction

    • Wenliang Chen, Min Zhang
    Pages 1-10

  3. Dependency Parsing Models

    • Wenliang Chen, Min Zhang
    Pages 11-25

  4. Overview of Semi-supervised Dependency Parsing Approaches

    • Wenliang Chen, Min Zhang
    Pages 27-31

  5. Training with Auto-parsed Whole Trees

    • Wenliang Chen, Min Zhang
    Pages 33-47

  6. Training with Lexical Information

    • Wenliang Chen, Min Zhang
    Pages 49-56

  7. Training with Bilexical Dependencies

    • Wenliang Chen, Min Zhang
    Pages 57-70

  8. Training with Subtree Structures

    • Wenliang Chen, Min Zhang
    Pages 71-105

  9. Training with Dependency Language Models

    • Wenliang Chen, Min Zhang
    Pages 107-126

  10. Training with Meta-features

    • Wenliang Chen, Min Zhang
    Pages 127-140

  11. Closing Remarks

    • Wenliang Chen, Min Zhang
    Pages 141-144
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Keywords

About this book

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.

Authors and Affiliations

  • Soochow University, Suzhou, China

    Wenliang Chen, Min Zhang

Bibliographic Information

  • Book Title: Semi-Supervised Dependency Parsing

  • Authors: Wenliang Chen, Min Zhang

  • DOI: https://doi.org/10.1007/978-981-287-552-5

  • Publisher: Springer Singapore

  • eBook Packages: Humanities, Social Sciences and Law, Social Sciences (R0)

  • Copyright Information: Springer Science+Business Media Singapore 2015

  • Hardcover ISBN: 978-981-287-551-8Published: 27 July 2015

  • Softcover ISBN: 978-981-10-1234-1Published: 23 October 2016

  • eBook ISBN: 978-981-287-552-5Published: 16 July 2015

  • Edition Number: 1

  • Number of Pages: VIII, 144

  • Number of Illustrations: 48 b/w illustrations, 13 illustrations in colour

  • Topics: Computational Linguistics

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