Natural Language Processing and Information Retrieval Principles and Applications

Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation. Discusses recent advancements in language-independent 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.