Graph Learning and Network Science for Natural Language Processing

4 Graph Embeddings for Natural Language Processing

Jyoti Gavhane, Rajesh Prasad, and Rajeev Kumar

DOI: 10.1201/9781003272649-4

CONTENTS

4.1 Introduction

4.1.1 Natural Language and Natural Language Processing

4.1.2 Processing: A Module in Machine Learning

4.2 Computational Techniques

4.2.1 How NLP Works

4.2.2 Graph Embeddings

4.2.2.1 Graph

4.2.2.2 Embedding

4.2.2.3 Graph Embeddings

4.2.2.4 Word Embeddings: Classic Example

4.3 The Singular Value Decomposition for “Graph Embeddings”

4.4 Predictive Methods

4.4.1 Word2vec

4.4.1.1 CBOW: Continuous Bag of Words

4.4.1.2 Skip-gram Model

4.5 More Embedding Techniques

4.6 Conclusion

4.7 Case Study: Neo4j Lab Implementations

References

4.1 Introduction

Graphs are frequently used in diverse real-world ...