Papers by Julia Bonn
Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus (2020.lrec-1)
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| Challenge: | Existing annotation tools for spatial relations capture fine-grained semantics and pragmatics derived from spatial information. |
| Approach: | They propose an extension to the Abstract Meaning Representation annotation schema that captures fine-grained spatial information in grounded corpora. |
| Outcome: | The proposed tool can handle fine-grained spatial relationships grounded in quantized space. |
Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)
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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao
| Challenge: | This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability. |
| Approach: | They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná . |
| Outcome: | The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed . |
From Spatial Relations to Spatial Configurations (2020.lrec-1)
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| Challenge: | Existing spatial representations are not sufficient for describing complex spatial configurations. |
| Approach: | They propose to integrate existing spatial representation languages with an annotation schema to extend the capabilities of existing ones. |
| Outcome: | The proposed language can represent a large set of spatial concepts crucial for reasoning . it integrates with the Abstract Meaning Representation (AMR) annotation schema and annotates text from diverse datasets . |
PropBank Comes of Age—Larger, Smarter, and more Diverse (2022.starsem-1)
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Sameer Pradhan, Julia Bonn, Skatje Myers, Kathryn Conger, Tim O’gorman, James Gung, Kristin Wright-bettner, Martha Palmer
| Challenge: | The PropBank has been used for semantic role labeling for over 20 years . it includes non-verbal predicates, adjectives, prepositions and multi-word expressions . |
| Approach: | They describe the evolution of the PropBank approach to semantic role labeling over the last 20 years . they describe the substantial effort that has gone into ensuring consistency and reliability of the various annotated datasets and resources . |
| Outcome: | The PropBank has been used for more than 20 years to test semantic role labeling systems. |
The New Propbank: Aligning Propbank with AMR through POS Unification (L18-1)
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| Challenge: | Existing Propbank corpus converts sense labels to a format which is more compatible with AMR and more robust to sparsity. |
| Approach: | They propose a corpus which converts existing Propbank sense labels to a new unified format which is more compatible with AMR and more robust to sparsity. |
| Outcome: | The proposed format is more compatible with AMR and robust to sparsity. |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
CAMRA: Copilot for AMR Annotation (2023.emnlp-demo)
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| Challenge: | Abstract Meaning Representation (AMR) is a formalism for deep lexical semantic representation. |
| Approach: | They introduce a web-based tool for constructing AMR from natural language text . CAMRA incorporates AMR parser models as coding co-pilots . |
| Outcome: | The proposed tool is based on the prototyping of existing AMR editors and integrates Propbank roleset lookup as an autocomplete feature. |
Bootstrapping UMR Annotations for Arapaho from Language Documentation Resources (2024.lrec-main)
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| Challenge: | Uniform Meaning Representation (UMR) is a graph-based semantic labeling system . it is based on the AMR family and is designed to be uniformly applicable to typologically diverse languages. |
| Approach: | They propose methods for bootstrapping UMR annotations for a given language from existing resources and typical language documentation products. |
| Outcome: | The proposed method generates enough basic structure in UMR graphs to automate labeling to a significant extent. |