Dependency Parsing
ISBN: 9783031021312
Platform/Publisher: SpringerLink / Springer International Publishing
Digital rights: Users: unlimited; Printing: unlimited; Download: unlimited
Subjects: Synthesis Collection of Technology (R0);

Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts

Sandra Kübler is Assistant Professor of Computational Linguistics at Indiana University, where she has worked since 2006. She received her M.A. from the University of Trier and her Ph.D. in Computational Linguistics from the University of Tubingen. Sandra's research focuses on data-driven methods for syntactic and semantic processing. For her dissertation work, she developed a novel memory-based approach to parsing spontaneous speech. This parser was integrated into the Verbmobil speech-to-speech translation system. Sandra is currently interested in parsing German, a non-configurational language, for which several treebanks are available. Her research focuses on comparisons between constituent-based and dependency-based parsing and comparisons of how different annotation schemes influence parsing results.

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