Record Nr.

UNINA9910627261503321

Autore

Pawar Sachin Sharad

Titolo

Investigations in entity relationship extraction / / Sachin Sharad Pawar, Pushpak Bhattacharyya, Girish Keshav Palshikar

Pubbl/distr/stampa


Singapore : , : Springer, , [2023]
©2023

ISBN

981-19-5391-0

Descrizione fisica

1 online resource (156 pages)

Collana

Studies in Computational Intelligence ; ; v.1058

Disciplina

929.374

Soggetti

Extraction (Linguistics)

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

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 -- 6.1.1 Using Span-Based Representation for Entity Mentions -- 6.1.2 Using BERT Embeddings -- 6.2 N-ary Cross-Sentence Relation Extraction -- 6.2.1 Standard Dataset -- 6.2.2 Graph Neural Networks -- 6.2.3 Using BERT Embeddings -- References -- 7 Conclusions -- 7.1 Summary of the Monograph -- 7.2 Future Directions -- References -- Appendix A Foundations -- A.1 Maximum Entropy Classifier -- A.2 Conditional Random Fields (CRF) -- A.3 Markov Logic Networks (MLN) -- A.3.1 Basics of First-order Logic -- A.3.2 Basics of MLNs -- A.3.3 Formal Definition -- A.3.4 Example -- A.3.5 Inference in MLNs -- A.4 Neural Networks -- A.4.1 Layers in Neural Networks -- A.4.1.1 Embedding Layer (Input Layer) -- A.4.1.2 Linear Layer -- A.4.1.3 Softmax Layer (Output Layer) -- A.4.2 Training Neural Networks.

A.4.3 Long Short-Term Memory Networks (LSTM) -- A.5 Support Vector Machines (SVM) with Kernels -- A.5.1 Kernel Functions -- A.5.2 String Subsequence Kernel -- A.5.3 Generalized Subsequence Kernel -- References.

Record Nr.

UNINA990009000130403321

Titolo

Lloyd's maritime and commercial law quarterly

Pubbl/distr/stampa


London, : Lloyd's of London Press

ISSN

0306-2945