de
en
Schliessen
Detailsuche
Bibliotheken
Projekt
Impressum
Datenschutz
zum Inhalt
Detailsuche
Schnellsuche:
OK
Ergebnisliste
Titel
Titel
Inhalt
Inhalt
Seite
Seite
Im Dokument suchen
Hakimov, Sherzod: Learning Multilingual Semantic Parsers for Question Answering over Linked Data. A comparison of neural and probabilistic graphical model architectures. 2019
Inhalt
Abstract
Acknowledgements
Introduction
Natural Language Interfaces
Semantic Parsing
Natural Language Question Answering System
Task Definition
Motivation
Research Questions
Contributions
Published Work
Chapter Structure
Preliminaries
RDF & Semantic Web
Semantic Web
RDF
RDF Vocabulary, RDF Schema and OWL
SPARQL
Linked Data
Knowledge Bases
Syntax
Categorial Grammar
Dependency Grammar
Semantics
Lexical Semantics
Distributional Semantics
Formal Semantics
Lambda Calculus
DUDES
Compositional Semantics
Semantic Composition with CCG and Lambda Calculus
Semantic Composition with Dependency Parse Trees and DUDES
Factor Graphs
Inference and Learning
Neural Networks
Datasets
Closed-domain
Geoquery
Open-domain
QALD
SimpleQuestions
Lexical Overlap
Dataset Complexity
Related Work
Semantic Parsing
QALD Systems
SimpleQuestions Systems
Lexicon
Mapping from text to knowledge base entries
Inverted Index
Resource Index
Property Index
Class Index
Restriction Class Index
Evaluation
CCG-based Semantic Parsing Approach
Overview
CCG-based Approach
Semantic parsing à la Zettlemoyer & Collins
Applying Semantic Parsing to QALD Dataset
Evaluation
Discussion
Dependency parse tree-based Semantic Parsing Approach
Overview
Dependency Parse Tree-based Approach
Representation with Factor Graphs
Inference
L2KB: Linking to Knowledge Base
QC: Query Construction
Candidate State Generation Algorithm
Semantic Composition
Features
L2KB Feature Template
QC Feature Template
L2KB Generated Features
QC Generated Features
Learning Model Parameters
Evaluation
Error Analysis
Discussion
Neural Network-based Semantic Parsing Approach
Overview
Methods
Inverted Index Construction for Entity Retrieval
Named Entity Recognition
Candidate Pair Generation
Model 1: BiLSTM-Softmax
Architecture
Hyper-parameters
Model 2: BiLSTM-KB
Graph Embedding
Architecture
Hyper-parameters
Model 3: BiLSTM-Binary
Architecture
Hyper-parameters
Model 4: FastText-Softmax
Hyper-parameters
Evaluation
Named Entity Recognition
Named Entity Linking
Predicate Prediction
Answer Prediction
Error Analysis
Discussion
Discussion
Overview
Manual Effort
CCG-based Semantic Parsing Approach
Dependency parse tree-based Semantic Parsing Approach
Neural Network-based Semantic Parsing Approach
Comparison
Syntax and Semantics Relationship
CCG-based Semantic Parsing Approach
Dependency parse tree-based Semantic Parsing Approach
Neural Network-based Semantic Parsing Approach
Comparison
Multilinguality
CCG-based Semantic Parsing Approach
Dependency parse tree-based Semantic Parsing Approach
Neural Network-based Semantic Parsing Approach
Comparison
Cross-domain Transferability
CCG-based Semantic Parsing Approach
Dependency parse tree-based Semantic Parsing Approach
Neural Network-based Semantic Parsing Approach
Comparison
Training Data Size & Search Space
Conclusion
Conclusion
Research Questions
Limitations
Future Work
Dependency parse tree-based Semantic Parsing Approach
Neural Network-based Semantic Parsing Approach
QALD Dataset
QALD-6 Instances from Training Data with Aggregation, Answer Types: Resource, Date, Number, Boolean
Bibliography