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Trends in Parsing Technology: Dependency Parsing, Domain Adaptation, and Deep Parsing 2010 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 298 pages, height x width: 235x155 mm, weight: 474 g, X, 298 p., 1 Paperback / softback
  • Sērija : Text, Speech and Language Technology 43
  • Izdošanas datums: 06-Dec-2012
  • Izdevniecība: Springer
  • ISBN-10: 9400733798
  • ISBN-13: 9789400733794
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  • Mīkstie vāki
  • Cena: 91,53 €*
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  • Formāts: Paperback / softback, 298 pages, height x width: 235x155 mm, weight: 474 g, X, 298 p., 1 Paperback / softback
  • Sērija : Text, Speech and Language Technology 43
  • Izdošanas datums: 06-Dec-2012
  • Izdevniecība: Springer
  • ISBN-10: 9400733798
  • ISBN-13: 9789400733794
Citas grāmatas par šo tēmu:
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.
Current Trends in Parsing Technology.- Single Malt or Blended? A Study
in Multilingual Parser Optimization.- A Latent Variable Model for Generative
Dependency Parsing.- Dependency Parsing and Domain Adaptation with
Data-Driven LR Models and Parser Ensembles.- Dependency Parsing Using Global
Features.- Dependency Parsing with Second-Order Feature Maps and Annotated
Semantic Information.- Strictly Lexicalised Dependency Parsing.- Favor Short
Dependencies: Parsing with Soft and Hard Constraints on Dependency Length.-
Corrective Dependency Parsing.- Inducing Lexicalised PCFGs with Latent
Heads.- Self-Trained Bilexical Preferences to Improve Disambiguation
Accuracy.- Are Very Large Context-Free Grammars Tractable?.- Efficiency in
Unification-Based N-Best Parsing.- HPSG Parsing with a Supertagger.-
Evaluating the Impact of Re-training a Lexical Disambiguation Model on Domain
Adaptation of an HPSG Parser.- Semi-supervised Training of a Statistical
Parser from Unlabeled Partially-Bracketed Data.