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Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature

preprint
submitted on 04.06.2019 and posted on 05.06.2019 by Leigh Weston, Vahe Tshitoyan, John Dagdelen, Olga Kononova, Kristin Persson, Gerbrand Ceder, Anubhav Jain
Over the past decades, the number of published materials science articles has increased manyfold. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw-text of published articles onto a structured database entry that allows for programmatic querying. To this end, we apply text-mining with named entity recognition (NER), along with entity normalization, for large-scale information extraction from the published materials science literature. The NER is based on supervised machine learning with a recurrent neural network architecture, and the model is trained to extract summary-level information from materials science documents, including: inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifer, with an overall accuracy (f1) of 87% on a test set, is applied to information extraction from 3.27 million materials science abstracts - the most information-dense section of published articles.
Overall, we extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. Our database shows far greater recall in document retrieval when compared to traditional text-based searches due to an entity normalization procedure that recognizes synonyms. We demonstrate that simple database queries can be used to answer complex \meta-questions" of the published literature that would have previously required laborious, manual literature searches to answer. All of our data has been made freely available for bulk download; we have also made a public facing application programming interface (https://github.com/materialsintelligence/matscholar) and website http://matscholar.herokuapp.com/search for easy interfacing with the data, trained models and functionality described in this paper. These results will allow researchers to access targeted information on a scale and with a speed that has not been previously available, and can be expected to accelerate the pace of future materials science discovery.

Funding

This work was supported by Toyota Research Institute through the Accelerated Materials Design and Discovery program.

History

Email Address of Submitting Author

lweston@lbl.gov

Institution

Lawrence Berkeley National Laboratory

Country

United States

ORCID For Submitting Author

0000-0002-3973-0161

Declaration of Conflict of Interest

No conflict of interest

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