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Trends in Parsing Technology

Dependency Parsing, Domain Adaptation, and Deep Parsing

  • Book
  • © 2010

Overview

Editors:
  1. Harry Bunt

    1. Tilburg University, Tilburg, Netherlands

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  2. Paola Merlo

    1. Dépt. Linguistique, Université de Genève, Genève, Switzerland

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    You can also search for this editor in PubMed   Google Scholar

  3. Joakim Nivre

    1. Uppsala, Sweden

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  • Collects contributions from many of today’s leading researchers in the area of natural language processing technology
  • Describes the contributors’ most recent work and a range of new techniques and results
  • Presents a state-of-the-art overview of current research in parsing tehcnologies with a focus on three important themes in the field today: dependency parsing, domain adaptation, and deep parsing

Part of the book series: Text, Speech and Language Technology (TLTB, volume 43)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-ix
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  2. Current Trends in Parsing Technology

    • Paola Merlo, Harry Bunt, Joakim Nivre
    Pages 1-17

  3. Single Malt or Blended? A Study in Multilingual Parser Optimization

    • Johan Hall, Jens Nilsson, Joakim Nivre
    Pages 19-33

  4. A Latent Variable Model for Generative Dependency Parsing

    • Ivan Titov, James Henderson
    Pages 35-55

  5. Dependency Parsing and Domain Adaptation with Data-Driven LR Models and Parser Ensembles

    • Kenji Sagae, Jun-ichi Tsujii
    Pages 57-68

  6. Dependency Parsing Using Global Features

    • Tetsuji Nakagawa
    Pages 69-86

  7. Dependency Parsing with Second-Order Feature Maps and Annotated Semantic Information

    • Massimiliano Ciaramita, Giuseppe Attardi
    Pages 87-104

  8. Strictly Lexicalised Dependency Parsing

    • Qin Iris Wang, Dale Schuurmans, Dekang Lin
    Pages 105-120

  9. Favor Short Dependencies: Parsing with Soft and Hard Constraints on Dependency Length

    • Jason Eisner, Noah A. Smith
    Pages 121-150

  10. Corrective Dependency Parsing

    • Keith Hall, Václav Novák
    Pages 151-167

  11. Inducing Lexicalised PCFGs with Latent Heads

    • Detlef Prescher
    Pages 169-182

  12. Self-Trained Bilexical Preferences to Improve Disambiguation Accuracy

    • Gertjan van Noord
    Pages 183-200

  13. Are Very Large Context-Free Grammars Tractable?

    • Pierre Boullier, Benoît Sagot
    Pages 201-222

  14. Efficiency in Unification-Based N-Best Parsing

    • Yi Zhang, Stephan Oepen, John Carroll
    Pages 223-241

  15. HPSG Parsing with a Supertagger

    • Takashi Ninomiya, Takuya Matsuzaki, Yusuke Miyao, Yoshimasa Tsuruoka, Jun-ichi Tsujii
    Pages 243-256

  16. Evaluating the Impact of Re-training a Lexical Disambiguation Model on Domain Adaptation of an HPSG Parser

    • Tadayoshi Hara, Yusuke Miyao, Jun-ichi Tsujii
    Pages 257-275

  17. Semi-supervised Training of a Statistical Parser from Unlabeled Partially-Bracketed Data

    • Rebecca Watson, Ted Briscoe, John Carroll
    Pages 277-291

  18. Back Matter

    Pages 293-297
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Keywords

About this book

Computer parsing technology, which breaks down complex linguistic structures into their constituent parts, is a key research area in the automatic processing of human language. This volume is a collection of contributions from leading researchers in the field of natural language processing technology, each of whom detail their recent work which includes new techniques as well as results. The book presents an overview of the state of the art in current research into parsing technologies, focusing on three important themes: dependency parsing, domain adaptation, and deep parsing. The technology, which has a variety of practical uses, is especially concerned with the methods, tools and software that can be used to parse automatically. Applications include extracting information from free text or speech, question answering, speech recognition and comprehension, recommender systems, machine translation, and automatic summarization. New developments in the area of parsing technology are thus widely applicable, and researchers and professionals from a number of fields will find the material here required reading. As well as the other four volumes on parsing technology in this series this book has a breadth of coverage that makes it suitable both as an overview of the field for graduate students, and as a reference for established researchers in computational linguistics, artificial intelligence, computer science, language engineering, information science, and cognitive science. It will also be of interest to designers, developers, and advanced users of natural language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.

Editors and Affiliations

  • Tilburg University, Tilburg, Netherlands

    Harry Bunt

  • Dépt. Linguistique, Université de Genève, Genève, Switzerland

    Paola Merlo

  • Uppsala, Sweden

    Joakim Nivre

Bibliographic Information

  • Book Title: Trends in Parsing Technology

  • Book Subtitle: Dependency Parsing, Domain Adaptation, and Deep Parsing

  • Editors: Harry Bunt, Paola Merlo, Joakim Nivre

  • Series Title: Text, Speech and Language Technology

  • DOI: https://doi.org/10.1007/978-90-481-9352-3

  • Publisher: Springer Dordrecht

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

  • Copyright Information: Springer Science+Business Media B.V. 2010

  • Hardcover ISBN: 978-90-481-9351-6Published: 14 October 2010

  • Softcover ISBN: 978-94-007-3379-4Published: 06 December 2012

  • eBook ISBN: 978-90-481-9352-3Published: 06 October 2010

  • Series ISSN: 1386-291X

  • Series E-ISSN: 2542-9388

  • Edition Number: 1

  • Number of Pages: X, 298

  • Topics: Computational Linguistics, Natural Language Processing (NLP)

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