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There is a critical need to automatically extract and synthesize knowledge and trends in nanotechnology research from an exponentially increasing body of literature. New engineered nanomaterials (ENMs) are continuously being discovered and Natural Language Processing (NLP) approaches can semi‐automate the cataloging of ENMs and their unique physico‐chemical properties. The potential for unintended consequences resulting from the commercialization of any emerging technology, including nanotechnology, underscores the need for risk assessment to keep pace with ENMs discovery and application. NLP approaches can be used to automatically aggregate studies on the exposure and hazard of ENMs as well as link the physicochemical properties to the measured effects.

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