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1 to 10 of 52 Results
Jul 31, 2020 - Cluster of Excellence - Asia and Europe in a Global Context
Arnold, Matthias; Dober, Agnes, 2020, "Cataloging Cultural Objects (CCO) – The CCO Commons examples in VRA Core 4 XML", https://doi.org/10.11588/data/KKTC9G, heiDATA, V1
“Cataloging Cultural Objects - a Guide to Describing Cultural Works and Their Images” (CCO) provides a data content standard for catalogers of cultural heritage. It is a guidebook for how to populate data elements and where to apply controlled vocabulary standards. The guide is f...
Jun 13, 2020 - Statistical Natural Language Processing Group
Beilharz, Benjamin; Sun, Xin, 2019, "LibriVoxDeEn - A Corpus for German-to-English Speech Translation and Speech Recognition", https://doi.org/10.11588/data/TMEDTX, heiDATA, V2
This dataset is a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The corpus consists of over 100 hours of audio material and over 50k parallel sentences. The speech data are low in disfluencies because of the...
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
Rehbein, Ines; Ruppenhofer, Josef; Do, Bich-Ngoc, 2020, "tweeDe", https://doi.org/10.11588/data/S90S35, heiDATA, V1
A German UD Twitter treebank, with >12,000 tokens from 519 tweets, annotated in the Universal Dependencies framework
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1
Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "A harmonised testsuite for social media POS tagging (DE)", https://doi.org/10.11588/data/KXLMHN, heiDATA, V1
A harmonised POS testsuite of web data, CMC and Twitter microtext, with word forms and STTS pos tags (+ some additional CMC-specific tags). UD pos tags have been automatically converted, based on the STTS pos tags. The data does not contain (manually corrected) lemma information....
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
Rehbein, Ines; Steen, Julius; Do, Bich-Ngoc; Frank, Anette, 2020, "Converter for content-to-head style syntactic dependencies", https://doi.org/10.11588/data/HE3BAZ, heiDATA, V1
A set of Python scripts that convert function-head style encodings in dependency treebanks in a content-head style encoding (as used in the UD treebanks) and vice versa (for adpositions, copula and coordination). For more information, see (Rehbein, Steen, Do & Frank 2017).
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
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.
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
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 - Empirical Linguistics and Computational Language Modeling (LiMo)
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.
Mar 6, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
van den Berg, Esther, 2020, "German Twitter Titling Corpus", https://doi.org/10.11588/data/AOSUY6, heiDATA, V1, UNF:6:xIy4tRguIiz8xpg52FlxOA== [fileUNF]
The German Titling Twitter Corpus consists of 1904 stance-annotated tweets collected in June/July 2018 mentioning 24 German politicians with a doctoral degree. The Addendum contains an additional 296 stance-annotated tweets from each month of 2018 mentioning 6 left-leaning and 4...
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