The Ins and Outs of Preposition Semantics: Challenges in Comprehensive Corpus Annotation and Automatic Disambiguation

Presented by: Nathan Schneider (Georgetown University)

Monday, October 31, 2022 

4:00-5:30 p.m. MST

UMC 247

In most linguistic meaning representations that are used in NLP, prepositions fly under the radar. I will argue that they should instead be put front and center given their crucial status as linkers of meaning—whether for spatial and temporal relations, for predicate-driven roles, or in special constructions. To that end, we have sought to characterize and disambiguate semantic functions expressed by prepositions and possessives in English (Schneider et al., ACL 2018; https://github.com/nertnlp/streusle/), and similar markers in other languages (Mandarin Chinese, Korean, Hindi, and German). This approach can be broadened to other constructions and integrated in full-sentence lexical semantic tagging as well as graph-structured meaning representation parsing. Other investigations include crowdsourced annotation, contextualized preposition embeddings, and preposition use in fluent nonnative English.

This is joint work with Vivek Srikumar, Jena Hwang, Archna Bhatia, Aryaman Arora, Na-Rae Han, Hanwool Choe, Meredith Green, Abhijit Suresh, Kathryn Conger, Tim O’Gorman, Austin Blodgett, Jakob Prange, Omri Abend, Sarah Moeller, Aviram Stern, Adi Shalev, Yilun Zhu, Yang Liu, Siyao Peng, Yushi Zhao, Nelson Liu, Michael Kranzlein, Daniel Hershcovich, Shira Wein, Luke Gessler, Emma Manning, Martha Palmer, and Ari Rappoport.