What I like about working on machine learning here at Elsevier is that we can combine theory with practice..— Georgios Tsatsaronis, Principal NLP Specialist
Machine Learning Engineer
The data that we have at Elsevier is a goldmine of information for a lot of interesting and intelligent things.
Natural language (processing) to me is the frontier of machine learning these days. Text is difficult to process, because it is not continuous and it is subjective: words mean different things to different people. But this is exactly what makes it fun. For example, we’ve built a functionality that distills the topic of a paper based on its contents so that we can now do large-scale analyses of which topics are cited most – and can therefore be considered to be trending. This helps researchers decide where they should focus their research on, it helps funding bodies decide where to invest their money in, and it helps publishers decide which journals should cover what next. The data that we have at Elsevier is a goldmine of information for a lot of interesting and intelligent things. The fact that we own this data and can therefore guarantee its quality really sets us apart.
Deep Kayal is a Machine Learning Engineer with Elsevier’s Content and Innovation Team, which works on the automatic extraction of data from research papers for various products at Elsevier.
More on Machine Learning from our tech employees
The challenge with automating healthcare technology is that the stakes are very high, since lives are on the line..— Sujit Pal, Technology Research Director
If machine learning could improve the quality of science, this would have huge implications on the price and speed of drug manufacturing..— Helena Deus, Senior Technology Researcher