Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing

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Harry Bunt, Paola Merlo, Joakim Nivre
Springer Science & Business Media, Oct 6, 2010 - Language Arts & Disciplines - 298 pages
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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 over 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 nautral language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.

 

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Contents

1 Current Trends in Parsing Technology
1
2 Single Malt or Blended? A Study in Multilingual Parser Optimization
18
3 A Latent Variable Model for Generative Dependency Parsing
35
4 Dependency Parsing and Domain Adaptation with DataDriven LR Models and Parser Ensembles
57
5 Dependency Parsing Using Global Features
69
6 Dependency Parsing with SecondOrder Feature Maps and Annotated Semantic Information
87
7 Strictly Lexicalised Dependency Parsing
105
Parsing with Soft and Hard Constraints on Dependency Length
121
10 Inducing Lexicalised PCFGs with Latent Heads
169
11 SelfTrained Bilexical Preferences to Improve Disambiguation Accuracy
183
12 Are Very Large ContextFree Grammars Tractable?
201
13 Efficiency in UnificationBased NBest Parsing
223
14 HPSG Parsing with a Supertagger
242
15 Evaluating the Impact of Retraining a Lexical Disambiguation Model on Domain Adaptation of an HPSG Parser
257
16 Semisupervised Training of a Statistical Parser from Unlabeled PartiallyBracketed Data
277
Index
292

9 Corrective Dependency Parsing
151

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