Intro
Preface
Acknowledgements
Contents
About the Authors
1 Introduction
1.1 Entities
1.2 Relations
1.2.1 Global- versus Mention-level Relations
1.3 Motivation
1.4 Research Gaps and Objectives
1.4.1 End-to-end Relation Extraction
1.4.2 N-ary Cross-sentence Relation Extraction
1.5 Organization of the Monograph
References
2 Literature Survey
2.1 Relation Extraction
2.1.1 Feature-based Methods
2.1.2 Kernel Methods
2.1.3 Neural Approaches
2.1.4 Datasets
2.1.5 Evaluation
2.2 Joint Entity and Relation Extraction

2.2.1 Motivating Example
2.2.2 Overview of Techniques
2.2.3 Joint Inference Techniques
2.2.4 Joint Models
2.2.5 Experimental Evaluation
2.3 N-ary Cross-sentence Relation Extraction
2.3.1 Extracting Cross-sentence Relations
2.3.2 Extracting N-ary and Cross-sentence Relations
References
3 Joint Inference for End-to-end Relation Extraction
3.1 Introduction
3.1.1 Problem Definition
3.1.2 Motivation for Joint Extraction
3.2 Background: Markov Logic Networks (MLN)
3.2.1 Basics of First-order Logic
3.2.2 Basics of MLNs
3.2.3 Formal Definition

3.2.4 Inference in MLNs
3.3 Building Blocks for Our Approach
3.3.1 Identifying Entity Mention Candidates
3.3.2 Entity Type Classifier
3.3.3 Entity Type Agnostic Relation Classifier
3.3.4 Pipeline Relation Classifier
3.4 Joint Extraction using Inference in Markov Logic Networks (MLN)
3.4.1 Motivation
3.4.2 Domains and Predicates
3.4.3 Generic Rules
3.4.4 Sentence-specific Rules
3.4.5 Additional Semantic Rules
3.4.6 Joint Inference
3.5 Example
3.6 Experimental Analysis
3.6.1 Limitations of Our Approach
References

4 Joint Model for End-to-End Relation Extraction
4.1 Motivation
4.2 All Word Pairs Model (AWP-NN)
4.2.1 Features for the AWP-NN Model
4.2.2 Architecture of the AWP-NN Model
4.3 Inference Using Markov Logic Networks
4.4 Experimental Analysis
4.4.1 Datasets
4.4.2 Implementation Details
4.4.3 Results
4.4.4 Analysis of Results
4.5 Domain-Specific Entities and Relations
4.5.1 Adverse Drug Reactions
4.5.2 TAC 2017: ADR Extraction Task
References
5 N-ary Cross-Sentence Relation Extraction
5.1 Introduction
5.2 Problem Definition

5.2.1 Comparison with Relevant Past Work
5.3 Proposed Approach
5.3.1 Constructing Sequence Representations
5.3.2 Constrained Subsequence Kernel (CSK)
5.3.3 Formal Definition of CSK
5.3.4 Classifying Candidate Relation Instances
5.4 Experimental Analysis
5.4.1 Datasets
5.4.2 Implementation Details
5.4.3 Analysis of Results and Errors
5.5 Discussion on Decomposition of N-ary Relations
5.5.1 Examples of Various Relation Types
5.5.2 Generalized Theorem
References
6 Recent Advances in Entity and Relation Extraction
6.1 Joint Entity and Relation Extraction