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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.


Why is now the time for artificial intelligence?

#artificialintelligence

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.


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

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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

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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 .