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41 to 50 of 85 Results
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...
Aug 16, 2023 - arthistoricum.net@heiDATA
Knaus, Gudrun; Kailus, Angela; Stein, Regine, 2022, "LIDO-Handbuch für die Erfassung und Publikation von Metadaten zu kulturellen Objekten - Band 2: Malerei und Skulptur [Anwendungsbeispiele]", https://doi.org/10.11588/data/CHEPS6, heiDATA, V3
LIDO (Lightweight Information Describing Objects) ist ein XML-Schema für die standardkonforme Bereitstellung von Metadaten über kulturelle Objekte in einer Vielzahl von digitalen Kontexten. Basierend auf diesem internationalen Standard dient das "LIDO-Handbuch für die Erfassung u...
Feb 26, 2024 - RATIO_EXPLAIN
Becker, Maria, 2024, "LLMs4Implicit-Knowledge-Generation Public", https://doi.org/10.11588/data/5VTJ26, heiDATA, V1
Code for equipping pretrained language models (BART, GPT-2, XLNet) with commonsense knowledge for generating implicit knowledge statements between two sentences, by (i) finetuning the models on corpora enriched with implicit information; and by (ii) constraining models with key c...
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, "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.
Aug 30, 2023 - Propylaeum@heiDATA
Mara, Hubert; Homburg, Timo, 2023, "MaiCuBeDa Hilprecht - Mainz Cuneiform Benchmark Dataset for the Hilprecht Collection", https://doi.org/10.11588/data/QSNIQ2, heiDATA, V1, UNF:6:NXlfO+rwTQYYtmBeze9QUw== [fileUNF]
Das Mainz Cuneiform Benchmark Dataset (MaiCuBeDa) beinhaltet Bilder von Keilschrifzeichen, Worten bestehend aus Keilschriftzeichen, Keilschrifzeichenzeilen und annotierten Einzelkeilen basierend auf dem Datenset HeiCuBeDa Hilprecht: https://doi.org/10.11588/data/IE8CCN . Die Anno...
Nov 2, 2016 - Perspektive Bibliothek
Boiger, Wolfgang, 2016, "MARC21-MARCXML-Konverter", https://doi.org/10.11588/data/10091, heiDATA, V1
Quellcode für eine Perl-Implementierung eines MARC21-MARCXML-Konverters.
Nov 17, 2021 - Medical Informatics
Benning, Nils-Hendrik; Knaup, Petra; Rupp, Rüdiger, 2021, "Measurement Performance of Activity Measurements with Newer Generation of Apple Watch in Wheelchair Users with Spinal Cord Injury: Manually and Device-Counted Pushes [Data]", https://doi.org/10.11588/data/P1HEGO, heiDATA, V1, UNF:6:br0+tP0XWfzu+FGO7V4qLw== [fileUNF]
This dataset contains the results (manually counted pushes and pushes counted by Apple Watch Series 4) of the study presented in the paper "Accuracy of Activity Measurements with Newer Generations of Apple Watch in Wheelchair Users with Spinal Cord Injury”
Oct 7, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Marasović, Ana, 2019, "Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]", https://doi.org/10.11588/data/ERDJDI, heiDATA, V1
Abstract Modal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal’s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based cl...
Aug 19, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Kotnis, Bhushan, 2019, "Negative Sampling for Learning Knowledge Graph Embeddings", https://doi.org/10.11588/data/YYULL2, heiDATA, V1
Reimplementation of four KG factorization methods and six negative sampling methods. Abstract Knowledge graphs are large, useful, but incomplete knowledge repositories. They encode knowledge through entities and relations which define each other through the connective structure o...
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