Deep Learning in Natural Language Processing

In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing. Deep learning has revolutionized a number of applications in artificial intelligence, including speech, vision, natural language, game playing, healthcare, and robotics. In particular, the recent striking success of deep learning in a wide variety of Natural Language Processing (NLP) application areas has been taken as a landmark of deep learning in one of the most important tasks in Artificial Intelligence. The book presents the state-of-the-art of deep learning research, and its applications in major NLP tasks including speech recognition, lexical analysis, parsing, knowledge graph, machine translation, information retrieval, question answering, sentiment analysis, social computing, spoken language understanding, and dialogue systems. The self-contained, comprehensive chapters have been written by leading researchers in the field. It appeals undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers and anyone interested in deep learning and natural language processing