Nathan Schneider is an Associate Professor jointly appointed in the Departments of Linguistics and Computer Science at Georgetown University, where he teaches and leads interdisciplinary research at the intersection of computational linguistics and natural language processing. He earned a B.A. in Computer Science and Linguistics from UC Berkeley (2004–2008) and a Ph.D. in Language Technologies from Carnegie Mellon University (2008–2014) under advisor Noah Smith. After post-doctoral research at the University of Edinburgh ILCC with Mark Steedman (2014–2016), he joined Georgetown as Assistant Professor (2016–2022) and was promoted to Associate Professor in 2022. His research centers on the linguistic foundations of NLP, focusing on computational, corpus-based approaches to meaning construction. Key themes include: Linguistic Structure: Design, annotation, parsing, and evaluation of syntactic and semantic frameworks such as UD, CCG, AMR, UCCA, and FrameNet. Adposition Semantics: Cross-linguistic description and computational modeling of prepositions and postpositions via the SNACS framework. Metalanguage: Analysis of explicit language about language in linguistics, education, and law, and leveraging such data for NLP. Uncertainty, Rarity, and Noise: Modeling sparse and noisy linguistic phenomena to improve robustness and interpretability. Across more than 100 peer-reviewed publications since 2015, Schneider’s work exhibits a steady trajectory from foundational annotation schemes (e.g., SNACS, CGELBank, UCCA) to neural-era evaluations using transformer models and large language models, with increasing attention to legal and cross-lingual applications. His 2025 corpus of papers demonstrates a focus on legal language processing, child language acquisition corpora, multilingual supervision, and probing LLMs. Scientific Awards & Honors NSF CAREER Award (publicized Dec 2022) Recognition of undergraduate mentee for research achievement (Aug 2023) Invited keynotes and distinguished talks at NASSLLI, MWE Workshop, Georgetown Law SOLID Symposium, UT Austin, Charles University, Allen Institute for AI, Mila-Quebec AI Institute, University of Toronto, Saarland University, Dagstuhl Seminar, and many others. Advising, Teaching & Service Schneider advises Ph.D. and master’s students in both Linguistics and Computer Science and leads the NERT lab. He has served as Program Co-Chair for LAW-MWE-CxG@COLING 2018, Area Chair for COLING 2018, Program Co-Chair for LAW@EACL 2017, and Tutorial Co-Chair for EMNLP 2017, and regularly teaches graduate courses such as LING/COSC-672 Advanced Semantic Representation. Labs & Teams He heads the NERT (Nathan’s Empirical Research Team) lab at Georgetown, an interdisciplinary group developing corpora, models, and tools for multilingual and cross-domain NLP, with current projects on legal text, child language, and adposition semantics.
