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1 to 10 of 109 Results
Jul 6, 2020 - Heidelberg University Language and Cognition Lab
Stutterheim, Christiane von; Lambert, Monique; Gerwien, Johannes, 2020, "Limitations on the role of frequency in L2 acquisition [Dataset]", https://doi.org/10.11588/data/ZMWDP5, heiDATA, V1
This data set contains files associated with a study on motion event encoding in three L1 (French, German, English) and two L2 (German, English) groups of speakers. The L2 speakers are native speakers of French. Participants viewed short video clips and described them spontaneous...
Jun 19, 2020 - Archäologische Quellen
Hornung, Sebastian; Gilhaus, Johannes; Glunz-Hüsken, Bettina, 2020, "Ergänzende Materialien zu: Rituell oder profan? Ein bronzezeitlicher Fundplatz in der bayerischen Donau-Aue", https://doi.org/10.11588/data/JZFWWW, heiDATA, V1
Grabungspläne; ergänzende Photographien
May 26, 2020 - Heidelberg Centre for Transcultural Studies (HCTS)
Brosius, Christiane; Michaels, Axel, 2020, "Nepal Heritage Documentation Project, NHDP", https://doi.org/10.11588/data/A9DCZA, heiDATA, V1
DANAM is the Digital Archive for Nepalese Art and Monuments and the heart of the Nepal Heritage Documentation Project (NHDP), located at the Heidelberg Centre of Transcultural Studies (HCTS) and the Academy of Sciences (AdW) and operated in cooperation with Saraf Foundation and t...
May 26, 2020
Open Research Data from the Heidelberg Centre for Transcultural Studies (HCTS).
May 18, 2020 - Propylaeum
Sperling, Heinz, 2020, "Römisches Ziegeln: Über die Prozesskette zum Betriebsmodell einer römischen Ziegelei", https://doi.org/10.11588/data/8CB4FQ, heiDATA, V2, UNF:6:hpuAbcqedxpV2RWSpWwj5Q== [fileUNF]
Algorithmen zur Ermittlung der maximalen Chargen- und Saisonkapazität eines römischen Ziegelofens und dessen Ressourcenbedarf sowie der Anzahl für eine bestimmte Menge zu produzierenden Ziegel (z. B. in einem Ziegelbau) benötigten Öfen und der Menge der zugehörigen Ressourcen.
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.
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