Semi-supervised learning

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Table of contents

1 Introduction 
1.1 Motivation
1.2 Contributions and outline
2 Preliminaries 
2.1 Resource Description Framework
2.2 Information extraction
2.2.1 Information extraction tasks
2.2.2 Machine learning for information extraction
2.2.3 Deep learning for information extraction
2.2.4 Metrics for evaluating information extraction quality
2.2.5 Text representation
2.3 Conclusion
3 State of the art of computational fact checking 
3.1 Claim extraction
3.1.1 Unsupervised approaches
3.1.2 Supervised methods
3.2 Reference source search
3.3 Related datasets
3.4 Claim accuracy assessment
3.4.1 Using external sources
3.4.2 Using a knowledge graph
3.4.3 Using linguistic features
3.4.4 Using user input
3.5 Fact checking challenges
3.5.1 Fake news challenge
3.5.2 Fact Extraction and VERification
3.5.3 Check worthiness
3.6 Automated end-to-end fact checking systems
3.7 Conclusion
4 Extracting linked data from statistic spreadsheets 
4.1 Introduction
4.2 Reference statistic data
4.2.1 INSEE data sources
4.2.2 Conceptual data model
4.3 Spreadsheet data extraction
4.3.1 Data cell identification
4.3.1.1 The leftmost data location
4.3.1.2 Row signature
4.3.1.3 Collect additional data cells
4.3.2 Identification and extraction of header cells
4.3.2.1 The horizontal border
4.3.2.2 Cell borders
4.3.2.3 Collect header cells
4.3.3 Populating the data model
4.4 Linked data vocabulary
4.5 Evaluation
4.6 Implementation
4.7 Related works
4.8 Conclusion and future works
5 Searching for truth in a database of statistics 
5.1 Introduction
5.2 Search problem and algorithm
5.2.1 Dataset search
5.2.2 Text processing
5.2.3 Word-dataset score
5.2.4 Relevance score function
5.2.4.1 Content-based relevance score function
5.2.4.2 Location-aware score components
5.2.4.3 Content- and location-aware relevance score
5.2.5 Data cell search
5.3 Evaluation
5.3.1 Datasets and queries
5.3.2 Experiments
5.3.2.1 Evaluation metric
5.3.2.2 Parameter estimation and results
5.3.2.3 Running time
5.3.2.4 Comparison against baselines
5.3.3 Web application for online statistic search
5.4 Implementation
5.5 Related works
5.6 Conclusion and future works
6 Statistical mentions from textual claims 
6.1 Introduction
6.2 Statistical claim extraction outline
6.3 Entity, relation and value extraction
6.3.1 Statistical entities
6.3.2 Relevant verbs and measurement units
6.3.3 Bootstrapping approach
6.3.4 Extraction rules
6.4 Evaluation
6.4.1 Evaluation of the extraction rules
6.4.2 Evaluation of the end-to-end system
6.5 Implementation
6.6 Related works
6.7 Conclusion and future works
7 Topics exploration and classification 
7.1 Corpus construction
7.2 Topic extraction
7.3 Topic classification
7.3.1 Preliminaries
7.3.2 Model training
7.3.3 Evaluation
7.4 Conclusion
8 Conclusion 
8.1 Summary
8.2 Perspectives
Bibliography