ANALYSIS AND NEWS
READING RESEARCH PAPERS IN ‘UNPRECEDENTED DEPTH’
Eduard Hovy, from the Language Technologies Institute of Carnegie Mellon University in Pennsylvania, is working on DARPA’s Big Mechanism project. Hovy is working with Anton Yuryev, a consultant with Elsevier R&D solutions’ professional services team, using Elsevier’s natural language processing tools to develop automated ‘deep-reading’ technologies
Can you explain what the main goals of the Big Mechanism project are and how it will impact future research?
The goal of the Big Mechanism initiative, in the words of DARPA, is to leapfrog state-of-the-art analytics by developing automated technologies to help explain the causes and effects that drive complicated systems such as diseases like cancer. The initiative has the potential to transform the models we use to research and develop cancer drugs, as well as boosting the development, optimisation and selection of more targeted treatments – all within the next few years. Research teams are in the process of creating computerised systems that will read scientific and medical research papers in unprecedented depth and detail. Through this deep reading they can both uncover and extract relevant information; then integrate those initial fragments of knowledge into computational models of cancer pathways that already exist, updating them accordingly. With these updated models the systems can produce hypotheses and predications about mechanisms that will in turn be tested by researchers.
How will creating automated ‘deep reading’ methods assist researchers? Essentially, automated deep reading means the machines ploughing through articles can ‘read’ them more like scientists do, instead of a more shallow, surface-level reading. Machines can make judgments about statements and findings, then extract only that information which either supports or adds to existing knowledge. This filtering process makes it
10 Research Information AUGUST/SEPTEMBER 2015
Carnegie Mellon University
much easier for the team to go to the next step – namely, providing accurate input for those doing the modelling. In addition, the system becomes semantically ‘smarter’ with each iteration, ultimately benefitting everyone who uses it, whether those are current or future industry, academic or government partners.
How does natural language processing assist in the process of ‘deep reading’? Project teams are made up of individuals with widely different areas of expertise, from biology and chemistry to informatics and visualisation. Communication is therefore critical. Different disciplines
‘The first challenge for our team is making sense of a vast amount of data from diverse sources’
often have their own language to describe the same phenomena, meaning an inability to find a common tongue can block a project’s progress. Natural language processing software is a vital tool in this regard. By standardising names and learning which variations have the same meaning, the software supports our understanding of both terminology and experimental methods.
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