This tutorial presents novel computational analytic methods capable of unlocking the human knowledge that’s been documented and archived in the unstructured text of hundreds of millions of scientific publications to extend scientific discovery beyond human capacity, and ways to automate experimental knowledge generation. The instructors will explore pathways for visualizing and comprehending knowledge propagation, evolution, and assessment of scientific research fronts, data-based hypothesis generation and methods for quantifying research impact within the scientific community and beyond.
In Part 3 of this three-part tutorial, Justin Fessler introduces the natural language processing tools of IBM Watson. Through an exploration of specific test cases, he will show how natural language processing afforded by Watson can be utilized to determine latent connections between different data, identify trends and suggest links between disparate domains. These tools can be useful to both existing researchers in fields as well as newcomers, to quickly explore the domain.