Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science

16 Dec 2019Huichen YangCarlos A. AguirreMaria F. De La TorreDerek ChristensenLuis BobadillaEmily DavichJordan RothLei LuoYihong TheisAlice LamT. Yong-Jin HanDavid ButtlerWilliam H. Hsu

This paper describes a machine learning and data science pipeline for structured information extraction from documents, implemented as a suite of open-source tools and extensions to existing tools. It centers around a methodology for extracting procedural information in the form of recipes, stepwise procedures for creating an artifact (in this case synthesizing a nanomaterial), from published scientific literature... (read more)

PDF Abstract

Evaluation Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.