Generation


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

#artificialintelligence

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.


Can Artificial Intelligence Replace The Content Writer?

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Content marketing automation currently involves two core technologies, both of which are components of AI. They are natural language processing (NLP) and natural language generation (NLG). Hoping to finish in the top ten after a 14th place finish last season, Leicester City splashed out 26.70 million in the summer transfer period. Current automated content creation tools are event-driven.


Instantaneous reports – Guy Perelmuter – Medium

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One of the branches of the United Nations is the International Labor Organization (ILO), which, among other duties, compiles data from the global job market. These data show the dominance of the service sector over the agricultural and manufacturing sectors with respect to the number of people employed. In light of technological advances, the agricultural sector -- a major source of income for underdeveloped countries -- employs an ever smaller share of the population. The reports in question are typically produced by systems that use Natural Language Generation (NLG).


Accenture develops artificial intelligence-powered solution for visually impaired

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NEW DELHI: Accenture today said it has developed an artificial intelligence powered solution to help visually impaired people improve the way they experience the world around them and enhance their productivity in the workplace. Accenture, plans to introduce Drishti to more than 100 visually impaired employees in India. Drishti, which means'vision' in Sanskrit, provides smart phone-based assistance using AI technologies such as image recognition, natural language processing and natural language generation capabilities to describe the environment of a visually impaired person. Initially developed and tested with 10 blind professionals through a collaboration with the National Association for the Blind in India, the solution provides narration to the user on the number of people in a room, their ages, genders and even emotions based on facial expressions.


Demand for artificial intelligence goes global - Information Age

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Synechron Inc., the global financial services consulting and technology services provider, has announced that nearly 60 financial institutions are set to implement artificial intelligence (AI) technology – with interest spanning across four continents. Currently, 57 financial institutions based in Europe, the US, Middle East and Asia are being helped to adopt AI technology by Synechron: 28% of these firms are based in Europe with UK headquartered institutions accounting for nearly half (45%) of the interest in Europe, and 23% of interest worldwide. A further 26% involve natural language processing or natural language generation. Most (54%) of the interest from the UK is centered on robotic process automation, while 43% of US firms and 30% of firms based on the European continent are interested in adopting natural language processing or natural language generation technology.


Another Example Of How Artificial Intelligence Will Transform News And Journalism

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The technology relies on natural language generation (NLG), a cornerstone of much of the progress which has been made in recent years thanks to artificial intelligence and automation. The PA project is known as RADAR – Reporters and Data and Robots – and relies on open data sets from government, local authorities and public services. Urbs Media editor-in-chief Gary Rogers told me that they initially started looking at the possibilities of generating stories for national media using open data sources, but soon realized that its highly geographically-segmented nature meant it was very well suited for local stories. "So instead of writing one story about a dataset – a national story – you could write 10 regional stories or 200 local authority-based stories.


Journalists, look out: Google is funding the rise of the AI news machine in Europe

Mashable

Among them is RADAR, a collaboration between the UK and Ireland's Press Association (PA) and Urbs Media, a startup that creates localized news stories using AI. That influx of cash will be used to create a service that will ramp up automated news efforts, with natural language generation (NLG) AI programs pumping out up to 30,000 stories per month across localized distribution networks starting in 2018. To start, the AI will be tasked with producing low-level stories from templates created by human writers. Wordsmith, an NLG AI program, has been producing automated stories for the U.S.'s Associated Press (AP) since 2014, and other traditional outlets like the New York Times and Los Angeles Times have used automation for low-level reporting.


?utm_source=dlvr.it&utm_medium=twitter

@machinelearnbot

We are open sourcing the code for evaluating several popular metrics for natural language generation that we have used in our paper Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation. This code computes pre-existing word-overlap-based and embedding-similarity-based metrics at once using a single command. We hope that, by making evaluation on these metrics convenient, it will facilitate comparisons in NLP and dialogue literature.



[R] [1705.10929] Adversarial Generation of Natural Language • r/MachineLearning

@machinelearnbot

I have also tried extensively to use WGAN's to generate language sequences. I just don't understand why it doesn't converge to results that are as good as Max Likelihood. Even with curriculum learning and peephole LSTM's, you would think it would converge to a good optimum but the results still show that max likelihood is a better approach /. Can anyone think of why this doesn't work better than Max Likelihood?