Natural Language Processing on Social Data
Oxtractor is an artificial intelligence platform that extracts actionable insights from social data applying natural language processing. Our state-of-the-art machine learning approach provides teams access to top quality content.
Unlock Deep Semantics
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.
Deeper understanding improves predictive capabilities and ultimately provides more effective engagement with the end users.
Deep Learning for NLP
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.
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.
As the world begins to turn away from fossil fuels and depend increasingly on renewable resources, the energy sector is presented with a problem. Renewables are simply not as reliable as oil and gas, as they are largely dependent on weather conditions such as sunny skies and windy days. In a world where we become fully dependent on renewables, there is concern that supply may not always be able to meet demand.
This supply problem is compounded with the complications of individuals, businesses, and municipalities becoming small-scale energy producers themselves by way of solar panels and individual storage units connected to the grid. These producer-consumers, having varying and unpredictable patterns of individual production and consumption create instability on shared grids.
Producer-consumers cannot safely connect to a central, nationwide grid until we have predictive software able to understand and manage localized energy flows. The energy industry needs a smart technology that can ensure that there is an equilibrium between supply and demand at all times.
Information extraction has a particular importance for the finance industry. Velocity of extracting key information provides key advantage for financial professionals. As an example, a finance company may need to know all the company takeovers that take place during a certain time span and the details of each acquisition.
Relation extraction has some particular importance in that context as extracting relations such as “company takeovers”, “mergers-acquisitions” has direct contribution to their decision making mechanisms.
Cognitive technologies could eventually revolutionise every facet of government operations, from virtual desktop assistants to applications that can govern large shifting systems. Indeed, they are already having a profound impact on government work in some countries, with more dramatic effects to come.
It’s highly unusual for a business improvement to increase speed, enhance quality and reduce costs at the same time, but cognitive technologies offer that tantalising possibility.
From intelligence perspective, similar problems exist for intelligence analysts. Large amounts of intelligence reports are needed to be analyzed to search for people involved in terrorism events, the weapons used, and the targets of the attacks. Understanding these documents at a deeper level differentiates the set of further actions that should be taken for critical decision making.
Sports excite people as a triumph of human effort. Behind the scenes there are a number of things that go into that triumph, and at the top is technology. The sports world today is getting tech savvy by combining natural, athletic talent with advanced analytics and even artificial intelligence (AI) to produce the best possible outcomes on the playing field. Everything from reviewing player performance, improving areas of weakness to even predicting optimum actions for future players is being tapped into.
When we look at the current state of the sports industry, stats and analytics play an important role on player and team decisions. From analyzing specific player information to the accuracy of plays versus specific opponents, data analytics teams are now a common thread among a team’s staff. As coaches and owners continue to strive for a better understanding of trends and data to improve a team’s winning chances, new technologies like AI are emerging as key opportunities to stay ahead of the competition. The future of sports technology is bright, new innovations in hardware and software combined with the human athletic performance will lead to a truly connected sports age for players, coaches and fans.
The increase in the amount of scientific publications related to Bioinformatics makes it impossible for biomedical researchers to track key information in all of these documents. It takes too much time to look for discoveries related to particular genes, proteins, or other biomedical entities.
Traditional approaches of information retrieval such as keyword matching does not fulfill the requirements of biomedical researchers as majority of the biomedical entities have synonyms and ambigous names. Knowledge-based population is crucial for biomedical domain where knowledge bases such as FlyBase has key importance in facilitating biomedical research.
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.