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Empirical Linguistics and Computational Language Modeling (LiMo) (Department of Computational Linguistics of Heidelberg University and Leibniz Institute for the German Language)
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1 to 10 of 27 Results
Jan 23, 2020
Daza, Angel, 2020, "Encoder-Decoder Model for Semantic Role Labeling", https://doi.org/10.11588/data/TOI9NQ, heiDATA, V1
Abstract (Daza & Frank 2019): We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need paral...
Markdown Text - 8.4 KB - MD5: 835ab4a78a83f8bea4d55dd6caa51837
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ZIP Archive - 42.5 MB - MD5: 5a525ee5066a01138845a1276c110956
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Tab-Delimited - 119.5 KB - MD5: 2e631a49b1fdd3ffe8a091bcb16482fa
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Mar 26, 2020
Rehbein, Ines; Ruppenhofer, Josef, 2020, "German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)", https://doi.org/10.11588/data/ZHI94V, heiDATA, V1
Annotations of causal verbs, nouns and prepositions in context and lexicon file for causal verbs, nouns and prepositions.
ZIP Archive - 1.6 MB - MD5: f928beb9f56c4a3e011941904872a4eb
Data
Markdown Text - 7.8 KB - MD5: 705940dda9344f994549436966482467
Documentation
Mar 26, 2020
Rehbein, Ines; Ruppenhofer, Josef; Steen, Julius, 2020, "MACE-AL", https://doi.org/10.11588/data/C2OQN4, heiDATA, V1
A method for detecting noise in automatically annotated sequence-labelled data, combining MACE (Hovy et al. 2013) with Active Learning.
Mar 26, 2020 - MACE-AL
ZIP Archive - 326.8 KB - MD5: 056a7e70a8f8b6e8fa72e3eead763d39
Code
Mar 26, 2020
Rehbein, Ines; Ruppenhofer, Josef, 2020, "MACE-AL-TREE", https://doi.org/10.11588/data/THPEBR, heiDATA, V1
An method for detecting noise in automatically annotated dependency parse trees, combining MACE (Hovy et al. 2013) with Active Learning.
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