Natural language processing (NLP) is quickly becoming one of the foundational big data technologies that will allow healthcare to move forward with complex analytics, according to a series of market reports predicting significant growth for NLP products over the next few years. As healthcare organizations seek new strategies for extracting insights from unstructured data from electronic health records, Internet of Things devices, imaging studies, and elsewhere, they will create an NLP marketplace worth $2.65 billion by 2021, says ReportsnReports. "The market is growing rapidly because of the huge surge in clinical data, increasing use of connected devices, and evolving consumer needs," the report says. Natural language processing may play an instrumental role in precision medicine, predictive analytics, population health management, clinical decision support, and EHR documentation improvement. The NLP market is divided into several segments: interactive voice response and speech analytics technologies, optical character recognition (OCR), automatic coding, text analytics, and pattern and image recognition.
This short paper is describing a demonstrator that is complementing the paper "Towards Cross-Media Feature Extraction" in these proceedings. The demo is exemplifying the use of textual resources, out of which semantic information can be extracted, for supporting the semantic annotation and indexing of associated video material in the soccer domain. Entities and events extracted from textual data are marked-up with semantic classes derived from an ontology modeling the soccer domain. We show further how extracted Audio-Video features by video analysis can be taken into account for additional annotation of specific soccer event types, and how those different types of annotation can be combined.
This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross. The toolkit implements the fully convolutional model described in Convolutional Sequence to Sequence Learning and features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU.
IT services provider Infosys on Thursday announced the launch of Infosys Nia, the next-generation Artificial Intelligence (AI) platform building on the success of the Company's first-generation AI platform, Infosys Mana, and its Robotic Process Automation (RPA) solution, AssistEdge. The new platform converges the big data/analytics, machine learning, knowledge management, and cognitive automation capabilities of Mana; end-to-end RPA capabilities of AssistEdge; advanced, high-performance and scalable machine learning capabilities of Skytree; and optical character recognition (OCR), natural language processing (NLP) capabilities and infrastructure management services. "We have seen tremendous adoption, and indeed, a massive embrace of Mana by our clients, particularly in leveraging Mana to improve service delivery and drive efficiencies and cost performance through automation. But we could clearly see that there was much more potential, an unlimited potential, in bringing AI to our clients' most sophisticated and complex business problems, as they work toward a vision of bringing technology to every aspect of their businesses," said Dr. Vishal Sikka, Chief Executive Officer, Infosys. "Nia, the next generation of our AI platform now takes our purposeful approach to AI, one in which technology serves to amplify people and empowers them to work in new ways, to new heights.