Welcome to your first step into the Natural Language Processing and Text Mining world! This is your risk-free approach (30-day refund policy) to delve deep into the fundamentals which Google, Amazon and Microsoft base themselves on when working with text data. Natural Language Processing is one of the most exciting fields in Data Science and Analytics nowadays. The ability to make a computer understand words and phrases is a technological innovation that brought a huge transformation to tasks such as Information Retrieval, Translation or Text Classification. In this course we are going to learn the fundamentals of working with Text data in Python and discuss the most important techniques that you should know to start your journey in Natural Language Processing.
The reality did not live up to the expectations set by science fiction. Accordingly, for many years, the majority of people's understanding of A.I. was confined to university laboratories, corporate skunk works, research parks, and that movie with Haley Joel Osment and Jude Law. Attempts to introduce A.I. products and services into the marketplace and for the broader benefits of society were ill-fated. Computing power was insufficient, and the abundance of structured data -- let alone a knowledge of what to do with said data -- was not yet upon us. A.I. has been on the cusp of the mainstream for the past 40 years, but 2016 is the year it's become a buzzword -- incorporating machine learning, natural language processing, voice recognition, and data mining, to name a few technologies.
Given the nature of our business, we often encounter confusion between Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU). To most folks, NLP is "Computers reading language." I mentioned NLU earlier; NLU stands for Natural Language Understanding, and is a specific type of NLP. The "reading" aspect of NLP is broad and encompasses a variety of applications, including things like: A more advanced application of NLP is NLU, ie.
This paper describes USI Answers -- a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation.