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E-raamat: Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing

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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.

This collection of contributions from leading researchers in the field of natural language processing technology details their recent work which includes new techniques as well as results. It focuses on dependency parsing, domain adaptation, and deep parsing.
1 Current Trends in Parsing Technology
1(18)
Paola Merlo
Harry Bunt
Joakim Nivre
2 Single Malt or Blended? A Study in Multilingual Parser Optimization
19(16)
Johan Hall
Jens Nilsson
Joakim Nivre
3 A Latent Variable Model for Generative Dependency Parsing
35(22)
Ivan Titov
James Henderson
4 Dependency Parsing and Domain Adaptation with Data-Driven LR Models and Parser Ensembles
57(12)
Kenji Sagae
Jun-Ichi Tsujii
5 Dependency Parsing Using Global Features
69(18)
Tetsuji Nakagawa
6 Dependency Parsing with Second-Order Feature Maps and Annotated Semantic Information
87(18)
Massimiliano Ciaramita
Giuseppe Attardi
7 Strictly Lexicalised Dependency Parsing
105(16)
Qin Iris Wang
Dale Schuurmans
Dekang Lin
8 Favor Short Dependencies: Parsing with Soft and Hard Constraints on Dependency Length
121(30)
Jason Eisner
Noah A. Smith
9 Corrective Dependency Parsing
151(18)
Keith Hall
Vaclav Novak
10 Inducing Lexicalised PCFGs with Latent Heads
169(14)
Dellef Prescher
11 Self-Trained Bilexical Preferences to Improve Disambiguation Accuracy
183(18)
Gertjan van Noord
12 Are Very Large Context-Free Grammars Tractable?
201(22)
Pierre Boullier Benoit Sagot
13 Efficiency in Unification-Based TV-Best Parsing
223(20)
Yi Zhang
Stephan Oepen
John Carroll
14 HPSG Parsing with a Supertagger
243(14)
Takashi Ninomiya
Takuya Matsuzaki
Yusuke Miyao
Yoshimasa Tsuruoka
Jun-Ichi Tsujii
15 Evaluating the Impact of Re-training a Lexical Disambiguation Model on Domain Adaptation of an HPSG Parser
257(20)
Tadayoshi Hara
Yusuke Miyao
Jun-Ichi Tsujii
16 Semi-supervised Training of a Statistical Parser from Unlabeled Partially-Bracketed Data
277(16)
Rebecca Watson
Ted Briscoe
John Carroll
Index 293