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Search Publications by: Jacob Collard (Fed)

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Displaying 1 - 4 of 4

Parmesan: mathematical concept extraction for education

May 20, 2024
Jacob Collard, Valeria de Paiva, Eswaran Subrahmanian
Mathematics is a highly specialized domain with its own unique set of challenges. Despite this, there has been relatively little research on natural language processing for mathematical texts, and there are few mathematical language resources aimed at NLP

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

August 8, 2023
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 the disease. This manifested itself in the creation of the COVID-19 Open Research Dataset (CORD

Extracting Mathematical Concepts from Text

October 12, 2022
Jacob Collard, Valeria de Paiva, Brendan Fong, Eswaran Subrahmanian
We investigate some different systems for extracting mathematical entities from texts in the mathematical field of category theory, as a first step for constructing a mathematical knowledge graph. We consider four different term extractors and compare

Making Semantic Structures Explicit: Developing and Evaluating Tools and Techniques to Support Understanding of Large Cybersecurity Corpora

February 4, 2022
Ira Monarch, Jacob Collard, Sangjin Shin, Eswaran Subrahmanian, Talapady N. Bhat, Ram D. Sriram
This report describes the adaptation, composition and use of natural language processing, machine learning and other computational tools to help make implicit informational structures in very large technical corpora explicit. The tools applied to the