no code implementations • SemEval (NAACL) 2022 • Jingxuan Tu, Eben Holderness, Marco Maru, Simone Conia, Kyeongmin Rim, Kelley Lynch, Richard Brutti, Roberto Navigli, James Pustejovsky
In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning.
no code implementations • 20 Oct 2022 • Jingxuan Tu, Kyeongmin Rim, Eben Holderness, James Pustejovsky
Understanding inferences and answering questions from text requires more than merely recovering surface arguments, adjuncts, or strings associated with the query terms.
no code implementations • 12 May 2021 • James Pustejovsky, Eben Holderness, Jingxuan Tu, Parker Glenn, Kyeongmin Rim, Kelley Lynch, Richard Brutti
In this paper, we argue that the design and development of multimodal datasets for natural language processing (NLP) challenges should be enhanced in two significant respects: to more broadly represent commonsense semantic inferences; and to better reflect the dynamics of actions and events, through a substantive alignment of textual and visual information.
no code implementations • WS 2019 • Elena Alvarez-Mellado, Eben Holderness, Nicholas Miller, Fyonn Dhang, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall
Predicting which patients are more likely to be readmitted to a hospital within 30 days after discharge is a valuable piece of information in clinical decision-making.
no code implementations • WS 2019 • Eben Holderness, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall
In this study, we undertook, to our knowledge, the first domain adaptation of sentiment analysis to psychiatric EHRs by defining psychiatric clinical sentiment, performing an annotation project, and evaluating multiple sentence-level sentiment machine learning (ML) models.
no code implementations • WS 2018 • Eben Holderness, Nicholas Miller, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Marie Meteer, Mei-Hua Hall
Readmission after discharge from a hospital is disruptive and costly, regardless of the reason.