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LINGUIST List 21.2949

Fri Jul 16 2010

Books: Computational Linguistics: Palmer, Gildea‌, Xue, Hirst (Ed)

Editor for this issue: Maria Moreno-Rollins <marialinguistlist.org>


Links to the websites of all LINGUIST's supporting publishers are available at the end of this issue.
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        1.    Andrea Descoteaux Hugg, Semantic Role Labeling: Palmer, Gildea‌, Xue, Hirst (Ed)

Message 1: Semantic Role Labeling: Palmer, Gildea‌, Xue, Hirst (Ed)
Date: 14-Jul-2010
From: Andrea Descoteaux Hugg <andreamorganclaypool.com>
Subject: Semantic Role Labeling: Palmer, Gildea‌, Xue, Hirst (Ed)
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Title: Semantic Role Labeling
Series Title: Synthesis Lectures on Human Language Technologies
Published: 2010
Publisher: Morgan & Claypool Publishers
               

Book URL: http://www.morganclaypool.com/doi/abs/10.2200/S00239ED1V01Y200912HLT006

Author: Martha Stone Palmer
Author: Daniel Gildea‌
Author: Nianwen Xue ‌
Editor: Graeme Hirst
Electronic: ISBN: 9781598298321 Pages: 103 Price: U.S. $ 30.00
Paperback: ISBN: 9781598298314 Pages: 103 Price: U.S. $ 40.00
Abstract:

This book is aimed at providing an overview of several aspects of semantic
role labeling. Chapter 1 begins with linguistic background on the definition of
semantic roles and the controversies surrounding them. Chapter 2 describes
how the theories have led to structured lexicons such as FrameNet, VerbNet
and the PropBank Frame Files that in turn provide the basis for large scale
semantic annotation of corpora. This data has facilitated the development of
automatic semantic role labeling systems based on supervised machine
learning techniques. Chapter 3 presents the general principles of applying
both supervised and unsupervised machine learning to this task, with a
description of the standard stages and feature choices, as well as giving
details of several specific systems. Recent advances include the use of joint
inference to take advantage of context sensitivities, and attempts to improve
performance by closer integration of the syntactic parsing task with semantic
role labeling. Chapter 3 also discusses the impact the granularity of the
semantic roles has on system performance. Having outlined the basic
approach with respect to English, Chapter 4 goes on to discuss applying the
same techniques to other languages, using Chinese as the primary example.
Although substantial training data is available for Chinese, this is not the
case for many other languages, and techniques for projecting English role
labels onto parallel corpora are also presented.

Table of Contents:
Preface
Semantic Roles
Available Lexical Resources
Machine Learning for Semantic Role Labeling
A Cross-Lingual Perspective
Summary

Linguistic Field(s): Computational Linguistics

Written In: English (eng )

See this book announcement on our website:
http://linguistlist.org/pubs/books/get-book.cfm?BookID=49306


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