Обложка Robust Semantic Role Labeling
Название книги:

Robust Semantic Role Labeling

Robust Semantic Role Labeling: Using Parsing Variations and Semantic Classes

LAP LAMBERT Academic Publishing (2015-05-25 )

Books loader

Omni badge имеющий право на ваучер
ISBN-13:

978-3-659-69196-6

ISBN-10:
3659691968
EAN:
9783659691966
Язык Книги:
Английский
Краткое описание:
Correctly identifying semantic entities and successfully disambiguating the relations between them and their predicates is an important and necessary step for successful natural language processing applications, such as text summarization, question answering, and machine translation. Researchers have studied this problem, semantic role labeling (SRL), as a machine learning problem since 2000. However, after using an optimal global inference algorithm to combine several SRL systems, the growth of SRL performance seems to have reached a plateau. Syntactic parsing is the bottleneck of the task of semantic role labeling and robustness is the ultimate goal. In this book, we investigate ways to train a better syntactic parser and increase SRL system robustness. We demonstrate that parse trees augmented by semantic role markups can serve as suitable training data for training a parser for an SRL system. For system robustness, we propose that it is easier to learn a new set of semantic roles. The new roles are less verb- dependent than the original PropBank roles. As a result, the SRL system trained on the new roles achieves significantly better robustness.
Издательский Дом:
LAP LAMBERT Academic Publishing
Веб-сайт:
http://www.lap-publishing.com/
By (author) :
Szu-ting Yi
Количество страниц:
172
Опубликовано:
2015-05-25
Акции:
В наличии
Категория:
Другое
Цена:
7010.35 руб
Ключевые слова:
Natural Language Processing, Machine Learning, Semantic Role Labeling

Books loader

Рассылка

Банковский перевод

  0 продуктов в корзине
Редактировать корзину
Loading frontend
LOADING