Zobacz książki o podobnej tematyce

Więcej informacji o Inductive Dependency Parsing

• Dane szczegółowe
• Opis
• Ulubione w innej kategorii

Za ten zakup dostaniesz 329 punkty

Opis

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.This book describes the framework of inductive dependency parsing, a methodology for robust and efficient syntactic analysis of unrestricted natural language text. Coverage includes a theoretical analysis of central models and algorithms, and an empirical evaluation of memory-based dependency parsing using data from Swedish and English. A one-stop reference to dependency-based parsing of natural language, it will interest researchers and system developers in language technology, and is suitable for graduate or advanced undergraduate courses.

Szczegóły książki

Kategoria Książki po angielsku Language linguistics Computational linguistics

• Pełny tytuł: Inductive Dependency Parsing
• Autor: Joakim Nivre
• Język: Angielski
• Oprawa: Twarda
• Liczba stron: 212
• EAN: 9781402048883
• ISBN: 1402048882
• ID: 01416758
• Wydawca: Springer-Verlag New York Inc.
• Waga: 1110 g
• Wymiary: 297 × 210 × 17 mm
• Data wydania: 28. June 2006

Ulubione w innej kategorii

---

---

---