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Application of graph rewriting to natural language processing

Application of graph rewriting to natural language processing (1회 대출)

자료유형
단행본
개인저자
Bonfante, Guillaume. Guillaume, Bruno. Perrier, Guy.
서명 / 저자사항
Application of graph rewriting to natural language processing / Guillaume Bonfante, Bruno Guillaume, Guy Perrier.
발행사항
London :   ISTE ;   Hoboken, NJ :   Wiley,   2018.  
형태사항
xx, 248 p. ; 25 cm.
총서사항
Cognitive science series. Logic, linguistics and computer science set ;volume 1
ISBN
9781786300966
서지주기
Includes bibliographical references (p. [241]-245) and index.
000 00000nam u2200205 a 4500
001 000045986767
005 20190617120012
008 190617s2018 enk b 001 0 eng d
020 ▼a 9781786300966
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 410.285 ▼2 23
084 ▼a 410.285 ▼2 DDCK
090 ▼a 410.285 ▼b B713a
100 1 ▼a Bonfante, Guillaume.
245 1 0 ▼a Application of graph rewriting to natural language processing / ▼c Guillaume Bonfante, Bruno Guillaume, Guy Perrier.
260 ▼a London : ▼b ISTE ; ▼a Hoboken, NJ : ▼b Wiley, ▼c 2018.
300 ▼a xx, 248 p. ; ▼c 25 cm.
490 1 ▼a Cognitive science series. Logic, linguistics and computer science set ; ▼v volume 1
504 ▼a Includes bibliographical references (p. [241]-245) and index.
700 1 ▼a Guillaume, Bruno.
700 1 ▼a Perrier, Guy.
830 0 ▼a Cognitive science and knowledge management series. ▼p Logic, linguistics and computer science set ; ▼v volume 1.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 410.285 B713a 등록번호 111810910 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Introduction -- 1. Programming with Graphs -- 1.1. Creating a graph -- 1.2. Feature structures -- 1.3. Information searches -- 1.3.1. Access to nodes -- 1.3.2. Extracting edges -- 1.4. Recreating an order -- 1.5. Using patterns with the GREW library -- 1.5.1. Pattern syntax -- 1.5.2. Common pitfalls -- 1.6. Graph rewriting -- 1.6.1. Commands -- 1.6.2. From rules to strategies -- 1.6.3. Using lexicons -- 1.6.4. Packages -- 1.6.5. Common pitfalls -- 2. Dependency Syntax: Surface Structure and Deep Structure -- 2.1. Dependencies versus constituents -- 2.2. Surface syntax: different types of syntactic dependency -- 2.2.1. Lexical word arguments -- 2.2.2. Modifiers -- 2.2.3. Multiword expressions -- 2.2.4. Coordination -- 2.2.5. Direction of dependencies between functional and lexical words -- 2.3. Deep syntax -- 2.3.1. Example -- 2.3.2. Subjects of infinitives, participles, coordinated verbs and adjectives -- 2.3.3. Neutralization of diatheses -- 2.3.4. Abstraction of focus and topicalization procedures -- 2.3.5. Deletion of functional words -- 2.3.6. Coordination in deep syntax -- 3. Graph Rewriting and Transformation of Syntactic Annotations in a Corpus -- 3.1. Pattern matching in syntactically annotated corpora -- 3.1.1. Corpus correction -- 3.1.2. Searching for linguistic examples in a corpus -- 3.2. From surface syntax to deep syntax -- 3.2.1. Main steps in the SSQ_to_DSQ transformation -- 3.2.2. Lessons in good practice -- 3.2.3. The UD_to_AUD transformation system -- 3.2.4. Evaluation of the SSQ_to_DSQ and UD_to_AUD systems -- 3.3. Conversion between surface syntax formats -- 3.3.1. Differences between the SSQ and UD annotation schemes -- 3.3.2. The SSQ to UD format conversion system -- 3.3.3. The UD to SSQ format conversion system -- 4. From Logic to Graphs for Semantic Representation -- 4.1. First order logic -- 4.1.1. Propositional logic -- 4.1.2. Formula syntax in FOL -- 4.1.3. Formula semantics in FOL -- 4.2. Abstract meaning representation (AMR) -- 4.2.1. General overview of AMR -- 4.2.2. Examples of phenomena modeled using AMR -- 4.3. Minimal recursion semantics, MRS -- 4.3.1. Relations between quantifier scopes -- 4.3.2. Why use an underspecified semantic representation? -- 4.3.3. The RMRS formalism -- 4.3.4. Examples of phenomenon modeling in MRS -- 4.3.5. From RMRS to DMRS -- 5. Application of Graph Rewriting to Semantic Annotation in a Corpus -- 5.1. Main stages in the transformation process -- 5.1.1. Uniformization of deep syntax -- 5.1.2. Determination of nodes in the semantic graph -- 5.1.3. Central arguments of predicates -- 5.1.4. Non-core arguments of predicates -- 5.1.5. Final cleaning -- 5.2. Limitations of the current system -- 5.3. Lessons in good practice -- 5.3.1. Decomposing packages -- 5.3.2. Ordering packages -- 5.4. The DSQ_to_DMRS conversion system -- 5.4.1. Modifiers -- 5.4.2. Determiners -- 6. Parsing Using Graph Rewriting -- 6.1. The Cocke–Kasami–Younger parsing strategy -- 6.1.1. Introductory example -- 6.1.2. The parsing algorithm -- 6.1.3. Start with non-ambiguous compositions -- 6.1.4. Revising provisional choices once all information is available -- 6.2. Reducing syntactic ambiguity -- 6.2.1. Determining the subject of a verb -- 6.2.2. Attaching complements found on the right of their governors -- 6.2.3. Attaching other complements -- 6.2.4. Realizing interrogatives and conjunctive and relative subordinates -- 6.3. Description of the POS_to_SSQ rule system -- 6.4. Evaluation of the parser -- 7. Graphs, Patterns and Rewriting -- 7.1. Graphs -- 7.2. Graph morphism -- 7.3. Patterns -- 7.3.1. Pattern decomposition in a graph -- 7.4. Graph transformations -- 7.4.1. Operations on graphs -- 7.4.2. Command language -- 7.5. Graph rewriting system -- 7.5.1. Semantics of rewriting -- 7.5.2. Rule uniformity -- 7.6. Strategies -- 8. Analysis of Graph Rewriting -- 8.1. Variations in rewriting -- 8.1.1. Label changes -- 8.1.2. Addition and deletion of edges -- 8.1.3. Node deletion -- 8.1.4. Global edge shifts -- 8.2. What can and cannot be computed -- 8.3. The problem of termination -- 8.3.1. Node and edge weights -- 8.3.2. Proof of the termination theorem -- 8.4. Confluence and verification of confluence -- Appendix: Mathematical Tools and Notation -- Bibliography -- Index -- Other titles from iSTE in Cognitive Science and Knowledge Management -- EULA -- .

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