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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...

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Bibliographic Details
Main Authors: Do, Bich-Ngoc (Author) , Rehbein, Ines (Author)
Format: Database Research Data
Language:English
Published: Heidelberg Universität 2023-11-13
DOI:10.11588/data/NNGPQZ
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Online Access: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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Author Notes:Bich-Ngoc Do, Ines Rehbein
Description
Summary: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: English, German and Czech that are used to report results in the paper.
Item Description:Produktionsdatum: 2020
Gesehen am 22.11.2023
Physical Description:Online Resource
DOI:10.11588/data/NNGPQZ

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