11 to 20 of 185 Results
Nov 13, 2023 -
Datasets for Dependency Tree Reranking
Gzip Archive - 12.3 MB -
MD5: e2a3ced99373c5e471d1cfc840b37c41
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Nov 13, 2023 -
Datasets for Dependency Tree Reranking
Gzip Archive - 11.1 MB -
MD5: 854d27091e1be9fc2bd3505fa5ec97a5
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Nov 13, 2023 -
Datasets for Dependency Tree Reranking
Gzip Archive - 35.0 MB -
MD5: 6d218b2ea37fe551338634e6d77aeb05
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Nov 13, 2023 -
Datasets for Dependency Tree Reranking
Markdown Text - 1.2 KB -
MD5: 647a783e60a80234a80ffbf5869a314b
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Nov 13, 2023 -
Datasets for Dependency Tree Reranking
Markdown Text - 1.0 KB -
MD5: e04a0cc53e6f6bc041a32d02befdc889
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Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines; Frank, Anette, 2023, "Head Selection Parsers and LSTM Labelers", https://doi.org/10.11588/data/BPWWJL, heiDATA, V1
This resource contains code, data and pre-trained models for various types of neural dependency parsers and LSTM labelers used in the papers: Do et al. (2017). "What Do We Need to Know About an Unknown Word When Parsing German" Do and Rehbein (2017). "Evaluating LSTM Models for G... |
Nov 13, 2023 -
Head Selection Parsers and LSTM Labelers
ZIP Archive - 58.8 KB -
MD5: 1994899f6e96118e147ea9565193198b
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Nov 13, 2023 -
Head Selection Parsers and LSTM Labelers
Markdown Text - 1.8 KB -
MD5: aa0872f2f4fb632dd0a09b2faccb9927
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Nov 13, 2023 -
Head Selection Parsers and LSTM Labelers
Markdown Text - 3.0 KB -
MD5: 7cb511b7bf6510aa7c1f9f7ba47a98d8
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Nov 13, 2023 -
Head Selection Parsers and LSTM Labelers
Markdown Text - 470 B -
MD5: 0c88b76a2a9e9d2de5c2f0ceeb605fa9
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