Collaborating Authors

Future Directions in Natural Language Processing: The Bolt Beranek and Newman Natural Language Symposium

AI Magazine

The Workshop on Future Directions in NLP was held at Bolt Beranek and Newman, Inc. (BBN), in Cambridge, Massachusetts, from 29 November to 1 December 1989. The workshop was organized and hosted by Madeleine Bates and Ralph Weischedel of the BBN Speech and Natural Language Department and sponsored by BBN's Science Development Program.

NLP Natural Language Processing Fundamentals in Python


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.

Natural Language Processing and Natural Language Generation: What's the Difference?


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.

Natural Language Access to Enterprise Data

AI Magazine

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.

Data Science:Data Mining & Natural Language Processing in R


Data Science:Data Mining & Natural Language Processing in R, Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples Created by Minerva SinghPreview this Course - GET COUPON CODE 100% Off Udemy Coupon .