This course provides a comprehensive introduction to natural language processing, focusing on fundamental concepts and techniques, as well as different tasks in natural language processing. Topics of this course include, but are not limited to: mathematical foundation for natural language processing, information theory, text representation, word segmentation and tokenization, part-of-speech tagging, syntactic and semantic parsing, named entity recognition, relation and event extraction, coreference resolution, sentiment analysis, reading comprehension, key-phrase extraction and generation, summarization, dialogue system, machine translation, etc. Students are expected to learn the latest research progress in this area and understand the necessary techniques as well as the tasks and use appropriate algorithms/models to finish a course project.
An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material.
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools.
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