no code implementations • LREC 2022 • Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M. Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, Clare Voss
We evaluate an annotation schema for labeling logical fallacy types, originally developed for a crowd-sourcing annotation paradigm, now using an annotation paradigm of two trained linguist annotators.
no code implementations • LREC 2022 • Luke Gessler, Nathan Schneider, Joseph C. Ledford, Austin Blodgett
We present Xposition, an online platform for documenting adpositional semantics across languages in terms of supersenses (Schneider et al., 2018).
1 code implementation • NAACL 2022 • Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O'Gorman, Young-suk Lee, Jeffrey Flanigan, Ramón Fernandez Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
Despite extensive research on parsing of English sentences into Abstraction Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation.
1 code implementation • ACL 2021 • Austin Blodgett, Nathan Schneider
We present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Ramon Fernandez Astudillo, Miguel Ballesteros, Tahira Naseem, Austin Blodgett, Radu Florian
Modeling the parser state is key to good performance in transition-based parsing.
Ranked #15 on AMR Parsing on LDC2017T10
no code implementations • LREC 2020 • Siyao Peng, Yang Liu, YIlun Zhu, Austin Blodgett, Yushi Zhao, Nathan Schneider
Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language.
no code implementations • WS 2019 • Austin Blodgett, Nathan Schneider
We define new semantics for the CCG combinators that is better suited to deriving AMR graphs.
no code implementations • 6 Dec 2018 • YIlun Zhu, Yang Liu, Siyao Peng, Austin Blodgett, Yushi Zhao, Nathan Schneider
This study adapts Semantic Network of Adposition and Case Supersenses (SNACS) annotation to Mandarin Chinese and demonstrates that the same supersense categories are appropriate for Chinese adposition semantics.
1 code implementation • ACL 2018 • Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend
Semantic relations are often signaled with prepositional or possessive marking--but extreme polysemy bedevils their analysis and automatic interpretation.
4 code implementations • 7 Apr 2017 • Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Archna Bhatia, Na-Rae Han, Tim O'Gorman, Sarah R. Moeller, Omri Abend, Adi Shalev, Austin Blodgett, Jakob Prange
This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 52 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github. com/nert-nlp/streusle/ ; version 4. 5 tracks guidelines version 2. 6).