Semantic Role Labeling (Google eBook)

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Morgan & Claypool Publishers, 2010 - Computers - 91 pages
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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
  

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Contents

Semantic Roles
1
12 LINGUISTIC BACKGROUND
2
13 MORE ELABORATED FRAMEWORKS
7
132 PROTOROLES
9
133 LEVINS VERB CLASSES AND ALTERNATIONS
14
134 FRAME SEMANTICS
18
Available Lexical Resources
21
22 VERBNET
22
342 VITERBI SEARCH
45
35 IMPACT OF PARSING
46
352 CHOICE OF SYNTACTIC REPRESENTATION
47
353 COMBINING PARSERS
48
36 EVALUATION
49
37 GENRE
50
39 UNSUPERVISED AND PARTIALLY SUPERVISED APPROACHES
51
A CrossLingual Perspective
53

23 PROPBANK
24
231 LIMITATIONS TO A VERBSPECIFIC APPROACH
26
24 SEMLINK
27
241 HIERARCHY OF THEMATIC ROLES
28
Machine Learning for Semantic Role Labeling
31
32 FEATURES USED FOR CLASSIFICATION
33
322 GOVERNING CATEGORY
35
324 POSITION
39
325 VOICE
40
327 SUBCATEGORIZATION
41
33 CHOICE OF MACHINE LEARNING METHOD
43
34 JOINT INFERENCE
44
41 SEMANTIC ROLE PROJECTION
56
42 SEMANTIC ROLE ALIGNMENT
59
43 LANGUAGEINDEPENDENT SEMANTIC ROLE LABELING
61
431 THE CHINESE PROPBANK
62
432 SEMANTIC ROLE LABELING FOR VERBS
63
433 SEMANTIC ROLE LABELING FOR NOUNS
70
434 SUMMARY
75
Summary
77
Bibliography
79
Authors Biographies
91
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University of Colorado, Boulder

University of Rochester

Brandeis University

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