Papers by Skatje Myers
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 . |
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation (2021.acl-long)
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| Challenge: | Compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. |
| Approach: | They propose a novel biomedical Information Extraction model to extract scientific entities and events from English research papers using Abstract Meaning Representation (AMR) they construct a sentence-level knowledge graph from an external knowledge base and encode it to improve the model's understanding of complex scientific concepts. |
| Outcome: | The proposed model can extract scientific entities and events from scientific literature and improve its understanding of complex scientific concepts. |
Leveraging Active Learning to Minimise SRL Annotation Across Corpora (2023.starsem-1)
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| Challenge: | In this paper, we investigate the application of active learning to semantic role labeling (SRL) using Bayesian Active Learning by Disagreement (BALD). |
| Approach: | They propose a sentence-focused selection method that is based off of previous methods of using model dropout to approximate a Gaussian process for SRL. |
| Outcome: | The proposed selection method improves on three different domain corpora on three domains with a large and diverse corpus. |
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. |
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications? (2024.findings-emnlp)
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Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar
| Challenge: | Numerical data is pivotal for medical questions and answers, but tabular data is not fully integrated into LLMs. |
| Approach: | They examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record data. |
| Outcome: | The proposed representations outperform those using raw numerical EHR data in medical diagnostics and prognostics. |
The Russian PropBank (2020.lrec-1)
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| Challenge: | Using proposition bank for Russian, we can automatically project semantic role labels from English to Russian. |
| Approach: | They propose a proposition bank for Russian that automatically projects semantic role labels from English to Russian. |
| Outcome: | The proposed resource automatically projectes semantic role labels from English to Russian. |