Trends in Parsing Technology

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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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.

• Computational Linguistics
• Natural Language Processing
• Parsing
• Parsing Technology
• artificial intelligence
• machine translation
• natural language
• semantic web technology
• speech recognition
• text mining

• Tilburg University, Tilburg, Netherlands

  Harry Bunt

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

  Paola Merlo

• Uppsala, Sweden

  Joakim Nivre

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