Machine INtelligence for Documents
Leverage the most advanced technologies in NLP and gain critical insights from structured and unstructured data, shortening the distance from raw data to insights to actions
Solutions
SocialMInD
Analyze targeted content from textual sources to extract 5 levels of user sentiment and 7 different emotions and identify offensive comments (hate speech).
HealthMInD
Automatic Information Extraction and Knowledge Discovery from medical literature to enhance decision making for health care and drug safety
LargeMInD
Leverage the wealth of information in small and large data repositories containing fragmented knowledge from heterogeneous information sources to improve document search and business decisions with intelligent data analysis
MIND Team
I. Vlahavas
Professor
Director of the Intelligent Systems Lab. Elected EurAI Fellow in 2017. Has been involved in 35+ R&D projects and co-authored 350+ papers.
Description
Leverage
the most advanced technologies in NLP and gain critical
insights from structured and unstructured data, shortening the
distance from raw data to insights to actions
Why Choose Our Team
Benefits
• Automatic extraction of important entities and their relations • Content classification • Multi language Support (EN & GR) • Domain agnostic application
Performance
-State of the art performance on English and Greek sources -State of the art content analysis on social media platforms
Technology
• Deep Neural Networks • Ontologies and Knowledge Bases • GPU/TPU accelerated inference • Pre trained models ready for production usage • Scalable model deployment • Multi GPU / Multi Cluster support