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Rachael Sexton (Fed)

Research Scientist

Rachael Sexton is a mechanical engineer in the Information Modeling and Testing Group at the Engineering Laboratory in the National Institute for Standards and Technology.
 
She is the project lead for the Knowledge Extraction & Application (KEA) Project, and a co-founder of the Technical Language Processing Community of Interest.
 
She researches the use of text analysis and network science for human-centric knowledge management in technical/domain-heavy situations. Her other research interests include design optimization, network analysis, research operations, and human-systems-integration.

Awards

2021 - DoC Bronze Medal (Nestor - annotation tool development)

2021 - DoC Bronze Medal (CORD19 data normalization effort - NIST Covid19 response)

 

Selected Publications

MSEC: A QUANTITATIVE RETROSPECTIVE

Author(s)
Rachael Sexton, Michael Brundage, Alden A. Dima, Michael Sharp
The Manufacturing Science and Engineering Conference (MSEC) in 2020 is the 15th annual conference put on by the Manufacturing Engineering Division (MED) of ASME

Technical Language Processing: Unlocking Maintenance Knowledge

Author(s)
Michael P. Brundage, Thurston B. Sexton, Melinda Hodkiewicz, Alden A. Dima, Sarah Lukens
Out-of-the-box natural-language processing (NLP) pipelines need re-imagining to understand and meet the requirements of the engineering sector. Text-based

Adapting natural language processing for technical text

Author(s)
Alden A. Dima, Sarah Lukens, Melinda Hodkiewicz, Thurston Sexton, Michael Brundage
Despite recent dramatic successes, Natural Language Processing (NLP) is not ready to address a variety of real-world problems. Its reliance on large standard

Publications

An Infrastructure for Curating, Querying, and Augmenting Document Data: COVID-19 Case Study

Author(s)
Eswaran Subrahmanian, Guillaume Sousa Amaral, Talapady N. Bhat, Mary C. Brady, Kevin G. Brady, Jacob Collard, Sarra Chouder, Philippe Dessauw, Alden A. Dima, John T. Elliott, Walid Keyrouz, Nicolas Lelouche, Benjamin Long, Rachael Sexton, Ram D. Sriram
With the advent of the COVID-19 pandemic, there was the hope that data science approaches could help discover means for understanding, mitigating, and treating

Adapting natural language processing for technical text

Author(s)
Alden A. Dima, Sarah Lukens, Melinda Hodkiewicz, Thurston Sexton, Michael Brundage
Despite recent dramatic successes, Natural Language Processing (NLP) is not ready to address a variety of real-world problems. Its reliance on large standard
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Created May 31, 2018, Updated May 25, 2023