Название: Natural Language Processing and Information Retrieval: Principles and ApplicationsАвтор: Muskan Garg, Sandeep Kumar, Abdul Khader
Издательство: CRC Press
Год: 2024
Страниц: 271
Язык: английский
Формат: pdf (true)
Размер: 17.1 MB
This book presents the basics and recent advancements in Natural Language Processing (NLP) and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining.
Federated Learning is a Machine Learning setting where multiple entities (clients) collaborate in solving a Machine Learning problem under the coordination of a central server or service provider. Each client’s raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective. Although researchers have been interested in privacy-preserved data analysis for several decades, only wide usage of the Internet and Machine Learning allowed the issue of using privacy-preserved data in training to become relevant.
Features:
• Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation
• Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data
• Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining
• Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing
• Covers latest datasets for natural language processing and information retrieval for social media like Twitter
The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.
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