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Education & Language - Linguistics | Trends in Parsing Technology - Dependency Parsing, Domain Adaptation, and Deep Parsing

Trends in Parsing Technology

Dependency Parsing, Domain Adaptation, and Deep Parsing

Bunt, Harry, Merlo, Paola, Nivre, Joakim (Eds.)

2011, X, 298 p.

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  • Assembles 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 an overview of the latest research in parsing technologies with a focus on three important themes in the field today: dependency parsing, domain adaptation, and deep parsing

Parsing technology is a central area of research in the automatic processing of human language. It is concerned with the decomposition of complex structures into their constituent parts, in particular with the methods, the tools and the software to parse automatically. Parsers are used in many application areas, such as information extraction from free text or speech, question answering, speech recognition and understanding, recommender systems, machine translation, and automatic summarization. New developments in the area of parsing technology are thus widely applicable.

This book collects contributions from leading researchers in the area of natural language processing technology, describing their recent work and a range of new techniques and results. The book 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.

This book is the fourth in a line of such collections, and its breadth of coverage should make it suitable both as an overview of the state 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.

Content Level » Research

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

Related subjects » Artificial Intelligence - Linguistics

Table of contents 

Current Trends in Parsing Technology, Paola Merlo, Harry Bunt and Joakim Nivre Single Malt or Blended? A Study in Multilingual Parser Optimization, Johan Hall, Jens Nilsson and Joakim Nivre A Latent Variable Model for Generative Dependency Parsing, Ivan Titov and James Henderson Dependency Parsing and Domain Adaption with Data-Driven LR Models and Parser Ensembles, Kenji Sagae and Jun’ichi Tsujii Dependency Parsing Using Global Features, Tetsuji Nakagawa Dependency Parsing with Second-Order Feature Maps and Annotated Semantic Information, Massimiliano Ciaramita and Guiseppe Attardi Strictly Lexicalised Dependency Parsing, Qin Iris Wang, Dale Schuurmans and Dekang Lin Favor Short Dependencies: Parsing with Soft and Hard Constraints on Dependency Length, Jason Eisner and Noah A. Smith Corrective Dependency Parsing, Keith Hall and Václav Novák Inducing Lexicalised PCFGs with Latent Heads, Detlef Prescher Self-Trained Bilexical Preferences to Improve Disambiguation Accuracy, Gertjan van Noord Are Very Large Context-Free Grammars Tractable? Pierre Boullier and Benoît Sagot Efficiency in Unification-Based N-Best Parsing, Yi Zhang, Stephan Oepen and John Carroll HPSG Parsing with a Supertagger, Takashi Ninomiya, Takuya Matsuzaki, Yusuke Miyao, Yoshimasa Tsuruoka and Jun‘ichi Tsujii Evaluating the Impact of Re-training a Lexical Disambiguation Model on Domain Adaption of an HPSG Parser, Tadayoshi Hara, Yusuke Miyao and Jun’ichi Tsujii Semi-supervised Training of a Statistical Parser from Unlabeled Partially-bracketed Data, Rebecca Watson, Ted Briscoe and John Carroll Index

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