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Creators/Authors contains: "Myers, Skatje"

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  1. ; ; ; ; ( , Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII))
    UMR-Writer is a web-based tool for annotating semantic graphs with the Uniform Meaning Representation (UMR) scheme. UMR is a graph-based semantic representation that can be applied cross-linguistically for deep semantic analysis of texts. In this work, we implemented a new keyboard interface in UMR-Writer 2.0, which is a powerful addition to the original mouse interface, supporting faster annotation for more experienced annotators. The new interface also addresses issues with the original mouse interface. Additionally, we demonstrate an efficient workflow for annotation project management in UMR-Writer 2.0, which has been applied to many projects. 
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    Free, publicly-accessible full text available July 1, 2024
  2. ; ; ; ; ; ( , The 17th Linguistic Annotation Workshop (LAW-XVII))
    Free, publicly-accessible full text available July 13, 2024
  3. ; ; ; ; ; ; ; ; ; ; et al ( , The 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023))
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Proceedings of the 21st International Workshop on Treebanks and Linguistic Theories (TLT, GURT/SyntaxFest 2023))
    This paper presents detailed mappings between the structures used in Abstract Meaning Representation (AMR) and those used in Uniform Meaning Representation (UMR). These structures include general semantic roles, rolesets, and concepts that are largely shared between AMR and UMR, but with crucial differences. While UMR annotation of new low-resource languages is ongoing, AMR-annotated corpora already exist for many languages, and these AMR corpora are ripe for conversion to UMR format. Rather than focusing on semantic coverage that is new to UMR (which will likely need to be dealt with manually), this paper serves as a resource (with illustrated mappings) for users looking to understand the fine-grained adjustments that have been made to the representation techniques for semantic categories present in both AMR and UMR. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ( , Proceedings of the 11th Joint Conference on Lexical and Computational Semantics)
    Vivi Nastase ; Ellie Pavlick ; Mohammad Taher Pilehvar ; Jose Camacho-Collados ; Alessandro Raganato (Ed.)
    This paper describes the evolution of the PropBank approach to semantic role labeling over the last two decades. During this time the PropBank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems. We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems 
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    Full Text Available 
  6. ; ; ; ; ; ( , Proc. The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021))
    Full Text Available 
  7. ; ; ; ; ( , Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020),)
    This paper presents a proposition bank for Russian (RuPB), a resource for semantic role labeling (SRL). The motivating goal for this resource is to automatically project semantic role labels from English to Russian. This paper describes frame creation strategies, coverage, and the process of sense disambiguation. It discusses language-specific issues that complicated the process of building the PropBank and how these challenges were exploited as language-internal guidance for consistency and coherence. 
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    Accepted Manuscript Full Text Available
  8. ; ( , Proceedings of the 1st Designing Meaning Representations Workshop, DMR-2019, held in conjunction with ACL,)
    This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the PropBank I dataset. 
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    Accepted Manuscript Full Text Available