Application of graph rewriting to natural language processing
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 th...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken, NJ :
ISTE Ltd. ; John Wiley & Sons, Inc
2018.
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| Colección: | Wiley ebooks.
Cognitive science series. Logic, linguistics and computer science set ; 1. |
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b40631953*spi |
Tabla de Contenidos:
- 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 dependency2.2.1. 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 corpora3.1.1. 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 Representation4.1. 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 Corpus5.1. 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.
