This book presents the basics and recent advancements in natural language processing 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.

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.

This book presents the basics and recent advancements in natural language processing 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.

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.

This book presents the basics and recent advancements in natural language processing 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.

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.