你好! Shipping to Taiwan with premium packaging for just NT$300 

Ship to
Taiwan
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Robust Semantic Role Labeling: Robust Semantic Role Labeling: Using Parsing Variations and Semantic Classes
Type
Physical Book
Publisher
Author
Language
English
Pages
172
Format
Paperback
ISBN13
9783659691966

Robust Semantic Role Labeling: Robust Semantic Role Labeling: Using Parsing Variations and Semantic Classes

Szu-Ting Yi (Author) · Lap Lambert Academic Publishing · Paperback

Robust Semantic Role Labeling: Robust Semantic Role Labeling: Using Parsing Variations and Semantic Classes - Szu-Ting Yi

New Book Imported to Taiwan
Delivery: 14 Oct - 22 Oct Shipping: 13 to 14 business days.
NT$ 2,643
NT$ 2,643

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Robust Semantic Role Labeling: Robust Semantic Role Labeling: Using Parsing Variations and Semantic Classes"

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Paperback.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews