Papers by Austin Blodgett
Comprehensive Supersense Disambiguation of English Prepositions and Possessives (P18-1)
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Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend
| Challenge: | Frequent prepositions like for are maddeningly polysemous, their interpretation depends especially on the object of the preposition. |
| Approach: | They propose a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. |
| Outcome: | The proposed annotations are comprehensive with respect to types and tokens of these markers and use broadly applicable supersense classes rather than fine-grained dictionary definitions. |
Semantic Supersenses for English Possessives (L18-1)
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| Challenge: | Existing semantic categories for possessive constructions are limited to nominals and s-genitives. |
| Approach: | They propose to use a supersense inventory to annotate English possessives . they show existing supersensor categories are readily applicable to possessives. |
| Outcome: | The proposed annotations are applied to English possessives in a corpus of web reviews. |
Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments (2021.acl-long)
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| Challenge: | Current algorithms for AMR parsing suffer from limited coverage and less-than-ideal accuracy . a new algorithm for AML uses unsupervised learning and heuristics to align components of AMR graphs to spans in English sentences . |
| Approach: | They propose algorithms for aligning components of Abstract Meaning Representation graphs to spans in English sentences. |
| Outcome: | The proposed approach covers a wider variety of AMR substructures than previously considered . it achieves higher coverage of nodes and edges, and does so with higher accuracy. |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
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Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
Xposition: An Online Multilingual Database of Adpositional Semantics (2022.lrec-1)
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| Challenge: | Xposition is an online platform for documenting adpositional semantics across languages . SNACS provides a unified metalanguage for characterizing the major classes of meanings expressed with appositions . |
| Approach: | They propose to use Xposition to document adpositional semantics across languages . Xpos houses annotation guidelines, structured lexicographic documentation, annotated corpora . |
| Outcome: | The proposed platform houses annotation guidelines, structured lexicographic documentation, and annotated corpora. |
The Search for Agreement on Logical Fallacy Annotation of an Infodemic (2022.lrec-1)
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Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M. Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, Clare Voss
| Challenge: | a parallel "infodemic" has emerged with the COVID-19 pandemic . logical fallacies can be subtly encoded in the structure of a document across multiple sentences . |
| Approach: | They evaluate an annotation schema for labeling logical fallacy types using linguist annotations . they propose to use a machine learning algorithm to train annotators for fallacy detection . |
| Outcome: | The proposed annotation schema is clear and non-overlapping for manual and system assignment. |
Transition-based Parsing with Stack-Transformers (2020.findings-emnlp)
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| Challenge: | Existing parsing systems use local or global models of the parser state to improve performance. |
| Approach: | They propose to modify the sequence-to-sequence Transformer to model global or local parser states in transition-based parsing. |
| Outcome: | The proposed model significantly improves performance on dependency and Abstract Meaning Representation (AMR) parsing tasks. |
A Corpus of Adpositional Supersenses for Mandarin Chinese (2020.lrec-1)
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| Challenge: | Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language. |
| Approach: | They propose to annotate Chinese adpositions in a corpus with all aforementioned supersenses . they adapt a framework that defined a set of supersens according to ostensibly language-independent criteria . |
| Outcome: | The proposed corpus is the first to be broadly annotated with adposition semantics in Chinese . it shows that the supersense categories are well-suited to Chinese adepositions despite syntactic differences from English . |