However, APIs like Watson Assistant or Amazon Lex make it easy to build services that can apply logic to observed patterns in those natural-language requests. These services may, for instance, observe a sudden rush of calls from an airport suffering take-off delays and change the sequence of options to prioritize rescheduling flights. Or they may see that calls from a particular country or region tend to be... - Source: dev.to / 19 days ago

Amazon's doesn't care about Mturk, they have their own AI that will eventually automate all their work too https://aws.amazon.com/lex/. Source: about 1 year ago

Amazon Lex, AWS's natural language conversational AI service. With Amazon Connect, it seamlessly leverages Amazon Transcribe to understand what is being said (speech-to-text), and Amazon Polly to provide the verbal response (text-to-speech). We aren't really using the Natural Language powers of Lex, but it has other uses for us:. - Source: dev.to / over 1 year ago

AWS has three high-quality tools: Amazon Lex, Amazon Rekognition, and Amazon SageMaker. - Source: dev.to / over 1 year ago

Introducing DTMF slot settings within Amazon Lex.Amazon Lex is a service for building conversational interfaces into any application using voice and text. With Amazon Lex, you can quickly and easily build conversational bots ("chatbots"), virtual agents, and interactive voice response (IVR) systems. Amazon Lex is excited to launch DTMF-only slot settings and configurable session attributes within the Lex console. - Source: dev.to / over 1 year ago

TL;DR: The authors pretrain the model to classify images into Wordnet synsets[a] that appear in the caption, using a standard Cross Entropy loss. They keep the number of classes relatively small by removing any synsets that don't show up in captions at least 500 times in the dataset. It seems to work well. My immediate question is: Why not classify among the entire hierarchy of all Wordnet synsets? --- [a]... - Source: Hacker News / about 2 months ago

To operationalize this intuition, the Microsoft and UC Berkeley researchers use WordNet and Wiktionary to augment the text in image-text pairs. The concept itself is augmented for isolated concepts, such as the class labels in ImageNet, whereas for captions (such as from GCC), the least common noun phrase is augmented. Equipped with this additional structured knowledge, contrastively pretrained models exhibit... - Source: dev.to / 3 months ago

If you like this, definitely check out WordNet (https://wordnet.princeton.edu/). - Source: Hacker News / 6 months ago

I didn't understand well what you meant, but maybe this site can help you: https://wordnet.princeton.edu/. Source: over 1 year ago

What I'd do is work with a huge database like WordNet and then try to "extrapolate" BIP39 to 4096 words by creating queries against WordNet to obtain words meeting the constraints you'd like to keep. Source: over 1 year ago