Max Thomas


2022

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Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
Mihai Surdeanu | John Hungerford | Yee Seng Chan | Jessica MacBride | Benjamin Gyori | Andrew Zupon | Zheng Tang | Haoling Qiu | Bonan Min | Yan Zverev | Caitlin Hilverman | Max Thomas | Walter Andrews | Keith Alcock | Zeyu Zhang | Michael Reynolds | Steven Bethard | Rebecca Sharp | Egoitz Laparra
Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing

An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none.When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one which does not scale well.Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision. We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour, to quickly build or extend a taxonomy to better fit information needs.

2017

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CADET: Computer Assisted Discovery Extraction and Translation
Benjamin Van Durme | Tom Lippincott | Kevin Duh | Deana Burchfield | Adam Poliak | Cash Costello | Tim Finin | Scott Miller | James Mayfield | Philipp Koehn | Craig Harman | Dawn Lawrie | Chandler May | Max Thomas | Annabelle Carrell | Julianne Chaloux | Tongfei Chen | Alex Comerford | Mark Dredze | Benjamin Glass | Shudong Hao | Patrick Martin | Pushpendre Rastogi | Rashmi Sankepally | Travis Wolfe | Ying-Ying Tran | Ted Zhang
Proceedings of the IJCNLP 2017, System Demonstrations

Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.

2015

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A Concrete Chinese NLP Pipeline
Nanyun Peng | Francis Ferraro | Mo Yu | Nicholas Andrews | Jay DeYoung | Max Thomas | Matthew R. Gormley | Travis Wolfe | Craig Harman | Benjamin Van Durme | Mark Dredze
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations