Inductive Dependency Parsing (Text, Speech and Language Technology, Band 34) - Softcover

Über die Autorin bzw. den Autor

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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This book provides an in-depth description of the framework of inductive dependency parsing, a methodology for robust and efficient syntactic analysis of unrestricted natural language text. This methodology is based on two essential components: dependency-based syntactic representations and a data-driven approach to syntactic parsing. More precisely, it is based on a deterministic parsing algorithm in combination with inductive machine learning to predict the next parser action.

The book includes a theoretical analysis of all central models and algorithms, as well as a thorough empirical evaluation of memory-based dependency parsing, using data from Swedish and English. Offering the reader a one-stop reference to dependency-based parsing of natural language, it is intended for researchers and system developers in the language technology field, and is also suited for graduate or advanced undergraduate education.