Deep text understanding is a key problem with the increasing amount of produced textual social data. Extracting such a structure of principal entity, related entities and aspects automatically has previously been deemed too challenging.

The extraction of aspects - target entities, their aspects and values - from social data streaming through digital channels is crucial for a better semantic representation. There is no NLP-based AI approach that automatically determines the aspects to extract and is capable of recognizing hierarchical aspect structures other than Oxtractor.

Given a set of product reviews or descriptions, word embeddings trained with the traditional deep learning models do not explicitly capture the domain relatedness of a token in a review just as they do not capture the sentiment information of the tokens in the reviews explicitly. However, for particular NLP tasks such as sentiment classification or aspect term extraction, it might be crucial to capture more than the syntactic contexts of the words.

For the tasks of aspect term extraction or opinion target extraction, predicting the domain-relatedness distribution of text based on input ngram is the primary solution to integrating domain information into word embeddings.

We apply logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real time simply means the analytics is completed within a few seconds or minutes after the arrival of new data.

Healthcare provider organizations spend a lot of money on customer service representatives taking patient inquiries via phone, e-mail or live chat. But there’s a way technology can step in and save healthcare organizations time and money: automated chat-bots infused with artificial intelligence.

Among organizations in various industries, healthcare providers most of all will benefit from increased use of chatbots, which are becoming more adept at their work because of advances in AI. Chat-bots could save organizations $8 billion annually worldwide by 2022, up from $20 million this year.

It is forecasted that healthcare and banking providers using bots can expect average time savings of just over four minutes per inquiry, equating to average cost savings in the range of $0.50-$0.70 per interaction.

Most chatbots use multiple technologies: natural language processing, knowledge management and sentiment analysis.

Some of the largest, most practical advancements in AI are happening in the industrial sector, and it might come to the surprise of more than a few that the retail industry — traditionally risk-averse and more fast follower than early adopter — is leading the way.

Among its new tech is an algorithm that learns about style from images, which it then uses to create fashion items from scratch. A basic AI fashion designer, if you will. It's far from ready to create the next Chanel line, but it gives an indication of what online retailers are preparing. It's not hard to envision the real world application of the program helping to boost online retailer's in-house brands.