Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction

no code implementations • NAACL (HCINLP) 2022 • 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

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.

Text Summarization

An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios

no code implementations • EMNLP (insights) 2020 • Andrew Zupon, Faiz Rafique, Mihai Surdeanu

Neural networks are a common tool in NLP, but it is not always clear which architecture to use for a given task.

Part-Of-Speech Tagging

Automatic Correction of Syntactic Dependency Annotation Differences

no code implementations • LREC 2022 • Andrew Zupon, Andrew Carnie, Michael Hammond, Mihai Surdeanu

Annotation inconsistencies between data sets can cause problems for low-resource NLP, where noisy or inconsistent data cannot be as easily replaced compared with resource-rich languages.

Dependency Parsing TAG

Text Normalization for Low-Resource Languages of Africa

no code implementations • 29 Mar 2021 • Andrew Zupon, Evan Crew, Sandy Ritchie

Training data for machine learning models can come from many different sources, which can be of dubious quality.