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E-raamat: Application of Graph Rewriting to Natural Language Processing

(Inria Nancy Grand-Est, France), (University of Lorraine, France), (University of Lorraine, France)
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  • Ilmumisaeg: 16-Apr-2018
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119522331
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Application of Graph Rewriting to Natural Language ProcessingSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-Apr-2018
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119522331
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The paradigm of Graph Rewriting is used very little in the field of Natural Language Processing. But graphs are a natural way of representing the deep syntax and the semantics of natural languages. Deep syntax is an abstraction of syntactic dependencies towards semantics in the form of graphs and there is a compact way of representing the semantics in an underspecified logical framework also with graphs. Then, Graph Rewriting reconciles efficiency with linguistic readability for producing representations at some linguistic level by transformation of a neighbor level: from raw text to surface syntax, from surface syntax to deep syntax, from deep syntax to underspecified logical semantics and conversely.

Introduction ix
Chapter 1 Programming with Graphs
1(34)
1.1 Creating a graph
2(3)
1.2 Feature structures
5(1)
1.3 Information searches
6(3)
1.3.1 Access to nodes
7(1)
1.3.2 Extracting edges
7(2)
1.4 Recreating an order
9(2)
1.5 Using patterns with the GREW library
11(9)
1.5.1 Pattern syntax
13(3)
1.5.2 Common pitfalls
16(4)
1.6 Graph rewriting
20(15)
1.6.1 Commands
22(2)
1.6.2 From rules to strategies
24(5)
1.6.3 Using lexicons
29(2)
1.6.4 Packages
31(1)
1.6.5 Common pitfalls
32(3)
Chapter 2 Dependency Syntax: Surface Structure and Deep Structure
35(36)
2.1 Dependencies versus constituents
36(6)
2.2 Surface syntax: different types of syntactic dependency
42(16)
2.2.1 Lexical word arguments
44(5)
2.2.2 Modifiers
49(2)
2.2.3 Multiword expressions
51(2)
2.2.4 Coordination
53(2)
2.2.5 Direction of dependencies between functional and lexical words
55(3)
2.3 Deep syntax
58(13)
2.3.1 Example
59(2)
2.3.2 Subjects of infinitives, participles, coordinated verbs and adjectives
61(1)
2.3.3 Neutralization of diatheses
61(3)
2.3.4 Abstraction of focus and topicalization procedures
64(2)
2.3.5 Deletion of functional words
66(2)
2.3.6 Coordination in deep syntax
68(3)
Chapter 3 Graph Rewriting and Transformation of Syntactic Annotations in a Corpus
71(32)
3.1 Pattern matching in syntactically annotated corpora
72(7)
3.1.1 Corpus correction
72(5)
3.1.2 Searching for linguistic examples in a corpus
77(2)
3.2 From surface syntax to deep syntax
79(13)
3.2.1 Main steps in the SSQ_to_DSQ transformation
80(3)
3.2.2 Lessons in good practice
83(7)
3.2.3 The UD_to_AUD transformation system
90(1)
3.2.4 Evaluation of the SSQ_to_DSQ and UD_to_AUD systems
91(1)
3.3 Conversion between surface syntax formats
92(11)
3.3.1 Differences between the SSQ and UD annotation schemes
92(6)
3.3.2 The SSQ to UD format conversion system
98(2)
3.3.3 The UD to SSQ format conversion system
100(3)
Chapter 4 From Logic to Graphs for Semantic Representation
103(40)
4.1 First order logic
104(4)
4.1.1 Prepositional logic
104(2)
4.1.2 Formula syntax in FOL
106(1)
4.1.3 Formula semantics in FOL
107(1)
4.2 Abstract meaning representation (AMR)
108(10)
4.2.1 General overview of AMR
109(4)
4.2.2 Examples of phenomena modeled using AMR
113(5)
4.3 Minimal recursion semantics, MRS
118(25)
4.3.1 Relations between quantifier scopes
118(2)
4.3.2 Why use an underspecified semantic representation?
120(2)
4.3.3 The RMRS formalism
122(11)
4.3.4 Examples of phenomenon modeling in MRS
133(4)
4.3.5 From RMRS to DMRS
137(6)
Chapter 5 Application of Graph Rewriting to Semantic Annotation in a Corpus
143(16)
5.1 Main stages in the transformation process
144(5)
5.1.1 Uniformization of deep syntax
144(1)
5.1.2 Determination of nodes in the semantic graph
145(2)
5.1.3 Central arguments of predicates
147(1)
5.1.4 Non-core arguments of predicates
147(1)
5.1.5 Final cleaning
148(1)
5.2 Limitations of the current system
149(1)
5.3 Lessons in good practice
150(4)
5.3.1 Decomposing packages
150(1)
5.3.2 Ordering packages
151(3)
5.4 The DSQ_to_DMRS conversion system
154(5)
5.4.1 Modifiers
154(2)
5.4.2 Determiners
156(3)
Chapter 6 Parsing Using Graph Rewriting
159(28)
6.1 The Cocke--Kasami--Younger parsing strategy
160(9)
6.1.1 Introductory example
160(3)
6.1.2 The parsing algorithm
163(1)
6.1.3 Start with non-ambiguous compositions
164(1)
6.1.4 Revising provisional choices once all information is available
165(4)
6.2 Reducing syntactic ambiguity
169(11)
6.2.1 Determining the subject of a verb
170(2)
6.2.2 Attaching complements found on the right of their governors
172(4)
6.2.3 Attaching other complements
176(3)
6.2.4 Realizing interrogatives and conjunctive and relative subordinates
179(1)
6.3 Description of the POS_to_SSQ rule system
180(5)
6.4 Evaluation of the parser
185(2)
Chapter 7 Graphs, Patterns and Rewriting
187(22)
7.1 Graphs
189(3)
7.2 Graph morphism
192(3)
7.3 Patterns
195(3)
7.3.1 Pattern decomposition in a graph
198(1)
7.4 Graph transformations
198(4)
7.4.1 Operations on graphs
199(1)
7.4.2 Command language
200(2)
7.5 Graph rewriting system
202(4)
7.5.1 Semantics of rewriting
205(1)
7.5.2 Rule uniformity
206(1)
7.6 Strategies
206(3)
Chapter 8 Analysis of Graph Rewriting
209(28)
8.1 Variations in rewriting
212(5)
8.1.1 Label changes
213(1)
8.1.2 Addition and deletion of edges
214(1)
8.1.3 Node deletion
215(1)
8.1.4 Global edge shifts
215(2)
8.2 What can and cannot be computed
217(3)
8.3 The problem of termination
220(9)
8.3.1 Node and edge weights
221(3)
8.3.2 Proof of the termination theorem
224(5)
8.4 Confluence and verification of confluence
229(8)
Appendix 237(4)
Bibliography 241(6)
Index 247
Guillaume Bonfante is a senior lecturer at the University of Lorraine, France.

Bruno Guillaume is a researcher at Inria Nancy Grand-Est, France.

Guy Perrier is Professor Emeritus at the University of Lorraine, France.
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