Over 10,000 physical typewritten documents from 1932 to 1941 had to be digitised, structured, and connected in order to create a single, centralised source of knowledge, for enabling the analysis of historical processes.
When privacy matters! A series of challenges for chatbots in data-sensitive businesses such as healthcare and finance by Christophe Willemsen
Meetup: Integration of Chatbots in Healthcare and BFSI, Dubai, 1.11.2018
Vlasta Kus talked about the advantages of graph-based natural language processing (NLP) using a public NASA dataset as example. From his abstract: “[…] we are building a platform (from large part open-source) that integrates Neo4j and NLP (such as Named Entity Recognition, sentiment analysis, word embeddings, LDA topic extraction), and we test and develop further related features and tools, lately, for example, integrating Neo4j and Tensorflow for employing deep learning techniques (such as deep auto-encoders for automatic text summarisation).”
During this talk, Christophe, Principal Consultant at GraphAware will walk you through the design of building Conversational Bots. To this end, he used Amazon Alexa and combined it with a Natural Language Processing stack backed by a Neo4j Graph Database.
You will discover the basics of an Amazon Alexa skill and how the user experience with voice devices can be enhanced with graph based algorithms such as recommendations.
