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

#artificialintelligence

The field of Artificial Intelligence (AI) is equal parts exciting and bewildering right now. Major advances are being made in a variety of areas, but following along is difficult because there are so many technical terms and acronyms. And don't even get me started on how many of the terms are similar. Given the nature of our business, we often encounter confusion between Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU). Until the last few years, NLP has been the more dynamic research area; the focus was on getting more data into the computer (e.g.


For Siri's New Competitor, SkyPhrase, Academia Isn't Big Enough for AI

AITopics Original Links

Academia is supposed to be a place where creative types can be free, and with that freedom accomplish great things, whether it be new art, breakthrough treatises, scientific discoveries, or feats of engineering. But academia isn't what it used to be, and to provide some insights into some of its problems, I compared notes with friend and former colleague, Nick Cassimatis, who is associate professor in the Department of Cognitive Science at Rensselaer. In our own ways, he and I have found severe limitations in academia today, limitations that led to my leaving academia to co-found a research institute, 2AI to be funded by intellectual property, and that led Nick to start his own company outside academia, SkyPhrase in order to achieve his ambitions in artificial intelligence. Nick's romantic ambitions started early โ€“ he began research into artificial intelligence and natural language at the precocious age of fifteen, and wrote a French-to-English translation program that helped put him on the Top-20 High School Students List by USA Today. More than simply artificial intelligence, his aim is to understand human-level intelligence, and how it can come about via many unintelligent parts.


NLP, NLU, NLG and how Chatbots work โ€“ Chatbot's Life

#artificialintelligence

Various acronyms and words are thrown around while talking about Chatbots and at first glance it seems they're all interchangeable with each other. To understand what the future of chatbots holds, let's familiarize ourselves with three basic acronyms.


Part-1: Introduction to Natural Language Processing (NLP)

#artificialintelligence

Natural language processing (NLP) is a field of artificial intelligence in which computers analyze, understand, and derive meaning information from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLP is characterized as a difficult problem in computer science. Human language is rarely precise or plainly spoken. To understand human language is to understand not only the words but the concepts and how they're linked together to create meaning.


Text Analytics: A Primer

@machinelearnbot

Editor's note: The following is an interview with University of Illinois professor and text analytics guru Bing Liu, conducted by marketing scientist Kevin Gray, in which Liu concisely outlines the current state of the field. Kevin Gray: I see "text analytics" and "text mining" used in various ways by marketing researchers and often used interchangeably. What do these terms mean to you? Bing Liu: My understanding is that the two terms mean the same thing. People from academia use the term text mining, especially data mining researchers, while text analytics is mainly used in industry. I seldom see academics use the term text analytics.