Collective and Statistical Analysis for Relation Extraction

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Table of contents

Abstract
Acknowledgements
1 Introduction
1.1 Research Objective
1.2 Contributions
1.3 Outline
2 Literature Review
2.1 Slot Filling Task
2.2 Slot Filling Systems
2.3 Relation Extraction
2.3.1 Relation Extraction Methods
2.3.2 Linguistic Features for Relation Characterization
2.3.3 Collective and Statistical Analysis for Relation Extraction .
2.3.4 Conclusion
2.4 Relation Validation
2.4.1 Ensemble Learning for Relation Validation
2.4.2 Graph based Methods for Relation Validation
2.4.3 Summary
2.5 Conclusion
3 Entity Graph and Measurements for Relation Validation
3.1 Graph Definition
3.2 Entity Graph and Graph Database
3.3 Graph Construction
3.4 Measurements on Graph
3.4.1 Node Centrality
3.4.2 Mutual Information
3.4.3 Network Density
3.4.4 Network Similarity
3.5 Relation validation by Graph Analysis
3.6 Conclusion
4 Linguistic Characteristics of Expressing and Validating Relations
4.1 Linguistically Motivated Classification of Relation
4.2 Syntactic Modeling
4.2.1 Syntactic Dependency Analysis
4.2.2 Dependency Patterns and Edit Distance
4.3 Lexical Analysis
4.3.1 Trigger Word Collection
4.3.2 Word Embeddings
4.3.3 Recognition of Trigger Words
4.4 Syntactic-Semantic Fusion
4.5 Evaluation of Word-embeddings
4.6 Conclusion
5 Relation Validation Framework
5.1 Relation Validation Model
5.1.1 Relation Validation Features
5.1.2 Relation Validation System Overview
5.2 Corpus and Preprocessing
5.2.1 KBP Slot Filling Corpora
5.2.2 KBP Slot Filling Responses and Snippet Assessments
5.3 Evaluation Metrics
5.4 Conclusion
6 Experiments and Results
6.1 Participation to TAC KBP-2016 SFV Task
6.1.1 Evaluation of Different Feature Groups
6.1.2 Relation Validation Models for KBP-2016 SFV Task
6.1.3 Conclusion
6.2 System Investigation
6.2.1 Statistical Difference Between TAC KBP Evaluation Datasets in 2015 and 2016
6.2.2 Impact of the Trustworthy Features
6.2.3 Impact of Trigger Words in the Slot Filling Responses
6.2.4 Identifying the Reason of Failure to Compute Graph Features
6.2.5 Conclusion and Plans for Improving the System
6.3 Supervised Relation Validation and Knowledge Base Population
6.3.1 Enlarging the Training and Testing Datasets
6.3.2 Relation Validation Models
6.3.3 Knowledge Base Population by Employing Relation Validation Models
6.4 An Experiment of Unsupervised Relation Validation and Knowledge Base Population
6.4.1 PageRank Algorithm
6.4.2 Graph Modeling
6.4.3 Evaluation
6.5 Summary
7 Conclusion and Future Work
7.1 Conclusion
7.2 Future Work
Bibliography