Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software. The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers. In this post, you will discover what natural language processing is and why it is so important. What Is Natural Language Processing? Photo by pedrik, some rights reserved.

Large data and fast computers mean that new and different things can be discovered from large datasets of text by writing and running software. In the 1990s, statistical methods and statistical machine learning began to and eventually replaced the classical top-down rule-based approaches to language, primarily because of their better results, speed, and robustness. The statistical approach to studying natural language now dominates the field; it may define the field. Data-Drive methods for natural language processing have now become so popular that they must be considered mainstream approaches to computational linguistics.

Top Machine Learning & Data Mining Books - in this post, we have scraped various signals (e.g. A highly rated book on Amazon written by a well-known author Christopher M. Bishop who is a distinguished Scientist at Microsoft Research in Cambridge where he leads the Machine Learning and Perception group. The "Machine Learning" is a well-know book in the field of Machine Learning written by Tom Mitchell - an American computer scientist professor from the Carnegie Mellon University. This foundational text is a comprehensive introduction to statistical natural language processing (NLP).

We'd be pleased to get feedback about how this book works out as a textbook, what is missing, or covered in too much detail, or what is simply wrong. Please send any feedback or comments to cmanning@acm.org