Neural rerankers for dependency parsing
This resource contains code for different types of neural rerankers (RCNN, RCNN-shared and GCN) from the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation". We also include in this resource the pre-trained models of different rerankers on 3 languages: En...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Dokumenttyp: | Datenbank Forschungsdaten |
| Sprache: | Englisch |
| Veröffentlicht: |
Heidelberg
Universität
2023-11-13
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| DOI: | 10.11588/data/NNGPQZ |
| Schlagworte: | |
| Online-Zugang: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.11588/data/NNGPQZ Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/NNGPQZ Verlag, kostenfrei, Volltext: https://github.com/bichngocdo/neural-tree-reranking 
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