Keith Alcock


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.

2019

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Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models
Rebecca Sharp | Adarsh Pyarelal | Benjamin Gyori | Keith Alcock | Egoitz Laparra | Marco A. Valenzuela-Escárcega | Ajay Nagesh | Vikas Yadav | John Bachman | Zheng Tang | Heather Lent | Fan Luo | Mithun Paul | Steven Bethard | Kobus Barnard | Clayton Morrison | Mihai Surdeanu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.