Brandon Stilson


2020

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CORD-19: The COVID-19 Open Research Dataset
Lucy Lu Wang | Kyle Lo | Yoganand Chandrasekhar | Russell Reas | Jiangjiang Yang | Doug Burdick | Darrin Eide | Kathryn Funk | Yannis Katsis | Rodney Michael Kinney | Yunyao Li | Ziyang Liu | William Merrill | Paul Mooney | Dewey A. Murdick | Devvret Rishi | Jerry Sheehan | Zhihong Shen | Brandon Stilson | Alex D. Wade | Kuansan Wang | Nancy Xin Ru Wang | Christopher Wilhelm | Boya Xie | Douglas M. Raymond | Daniel S. Weld | Oren Etzioni | Sebastian Kohlmeier
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19.

2017

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Interactive Visualization for Linguistic Structure
Aaron Sarnat | Vidur Joshi | Cristian Petrescu-Prahova | Alvaro Herrasti | Brandon Stilson | Mark Hopkins
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We provide a visualization library and web interface for interactively exploring a parse tree or a forest of parses. The library is not tied to any particular linguistic representation, but provides a general-purpose API for the interactive exploration of hierarchical linguistic structure. To facilitate rapid understanding of a complex structure, the API offers several important features, including expand/collapse functionality, positional and color cues, explicit visual support for sequential structure, and dynamic highlighting to convey node-to-text correspondence.