Automated Extraction of Chemical Synthesis Procedures Using Machine Learning

The project involves the development of a system to automate the extraction of synthesis procedures from the texts of organic chemistry journal articles that describe explicit, experimental syntheses of organic compounds and their corresponding properties.

Faculty Supervisor:

Jian Tang

Student:

Michael Guarino

Partner:

CognitiveChem Solutions Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

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