This Friday we'll have two talks followed by drinks.
16:00 Georgios Tsatsaronis (Elsevier) Topic Pages: From Articles to Answers
Automating the process of learning definitions from unstructured text at scale enables applications with great impact, such as building glossaries, dictionaries, or topic pages that may profile scientific concepts and help readers of scientific articles understand the contents faster and in depth. In this talk we are introducing Topic Pages, a publicly available set of automatically created information pages for scientific concepts across 21 domains. We are discussing the technical challenges pertaining to extracting the relevant information from tens of millions of book chapters and scientific articles, as well as the novel methodologies and architecture that were used, sitting at the borders of Machine Learning, Natural Language Processing and Scalable Data Processing and Management. The focus will be given on the best technical practices utilized to create this large scale machine learning production pipeline, as well as on the novel methodology used to learn textual definitions from unstructured text, based on Multiview LSTMs.
Bio:
Dr. George Tsatsaronis is Vice President Data Science, Research Content Operations, at Elsevier (RELX Group). Prior to joining Elsevier in 2016 he worked in academia for 13 years, doing research and teaching in the fields of machine learning, natural language processing and bioinformatics in universities in UK, Greece, Norway and Germany. He has published more than 60 scientific articles in high impact peer review journals and conference proceedings in various areas of Artificial Intelligence, primarily natural language processing and text mining. His PhD is in the field of text mining, and he also holds a BSc in Informatics from Athens University of Economics and Business, and an MSc in Advanced Computing from Imperial College London, with specialization in Artificial Intelligence and robotics. He is the inventor of several Artificial Intelligence pipelines that support some of the biggest research platforms of Elsevier.
