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Part 1: Document Description
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Citation |
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Title: |
AMR parse quality prediction [Source Code] |
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Identification Number: |
doi:10.11588/data/STHBGW |
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Distributor: |
heiDATA |
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Date of Distribution: |
2019-07-12 |
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Version: |
1 |
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Bibliographic Citation: |
Opitz, Juri, 2019, "AMR parse quality prediction [Source Code]", https://doi.org/10.11588/data/STHBGW, heiDATA, V1 |
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Citation |
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Title: |
AMR parse quality prediction [Source Code] |
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Identification Number: |
doi:10.11588/data/STHBGW |
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Authoring Entity: |
Opitz, Juri (Department of Computational Linguistics, Heidelberg University, Germany) |
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Date of Production: |
2019 |
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Distributor: |
heiDATA |
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Access Authority: |
Opitz, Juri |
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Holdings Information: |
https://doi.org/10.11588/data/STHBGW |
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Study Scope |
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Keywords: |
Arts and Humanities, Computer and Information Science, Abstract Meaning Representation (AMR), textual semantic analysis, knowledge graph, NLP |
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Topic Classification: |
semantic parsing |
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Abstract: |
<p>Accuracy prediction for AMR parsing predicts 33 accuracy metrics for a given sentence and its (automatic) AMR parse</p> <p><strong>Abstract (Opitz and Frank, 2019):</strong></p> <p>Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition of roles, following Dowty's feature-based view of proto-roles. This theory determines agenthood vs. patienthood based on a participant's instantiation of more or less typical agent vs. patient properties, such as, for example, volition in an event. To perform SPRL, we develop an ensemble of hierarchical models with self-attention and concurrently learned predicate-argument-markers. Our method is competitive with the state-of-the art, overall outperforming previous work in two formulations of the task (multi-label and multi-variate Likert scale prediction). In contrast to previous work, our results do not depend on gold argument heads derived from supplementary gold tree banks.</p> <p> </p> |
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Kind of Data: |
program source code |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
<p>Opitz, J. and Frank, A. (2019). Automatic Accuracy Prediction for AMR Parsing. In <em>Proceedings of the 8th Joint Conference on Lexical and Computational Semantics (*SEM)</em>, June 6-7 2019, Minneapolis, USA.</p> |
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Identification Number: |
1904.08301v1 |
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Bibliographic Citation: |
<p>Opitz, J. and Frank, A. (2019). Automatic Accuracy Prediction for AMR Parsing. In <em>Proceedings of the 8th Joint Conference on Lexical and Computational Semantics (*SEM)</em>, June 6-7 2019, Minneapolis, USA.</p> |
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Label: |
quamr.zip |
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Notes: |
application/zip |