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Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models.
Computers are machines, and cannot understand the free-flowing language used by humans for communication. They understand the language of 0s and 1s, which is a machine language called binary language. Without processing natural language, it’s difficult for humans to talk to computers. For this reason, an artificial intelligence-based solution called natural language processing (NLP) has been developed. NLP techniques help computers to interpret, understand and manipulate human language.
Features
Presents a comprehensive study of the interdisciplinary graphical approach to NLP
Covers recent computational intelligence techniques for graph-based neural network models
Discusses advances in random walk-based techniques, semantic webs, and lexical networks
Explores recent research into NLP for graph-based streaming data
Reviews advances in knowledge graph embedding and ontologies for NLP approaches
This book is aimed at researchers and graduate students in Computer Science, natural language processing, and Deep and Machine Learning