Graph Learning and Network Science for Natural Language Processing

By: Muskan Garg (Editor), Amit Kumar Gupta (Editor), Rajesh Prasad (Editor)

Write A Review

Hardcover | 28 December 2022 | Edition Number 1

Sorry, we are not able to source the book you are looking for right now.

We did a search for other books with a similar title, however there were no matches. You can try selecting from a similar category, click on the author's name, or use the search box above to find your book.

Advances in Graph based Natural Language Processing (NLP) and Information Retrieval (IR) tasks have shown the importance of processing Graph of Words. This book contains recent and concrete information from basics to advanced level about Graph based Learning like Neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph based natural language processing. This contains information about language generation based on the graphical theories and language models.

Features:

-Presents comprehensive study for the interdisciplinary graphical approach for natural language processing (NLP)

-Covers recent computational intelligence techniques for graph based neural network models

-Discusses advances in Random Walk based techniques, Semantics Web and Lexical Networks

-Explores recent research over Natural Language Processing for Graph based streaming data

-Reviews advances in Knowledge Graph Embedding and Ontologies for NLP Approaches

This book is aimed at researchers, graduate students in computer science, natural language processing, deep, and machine learning.

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $79.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.