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21 to 30 of 64 Results
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...
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 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...
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.
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...
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...
Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Wiegand, Michael, 2019, "Lexicon of Abusive Words (EN)", https://doi.org/10.11588/data/MKPEYV, heiDATA, V1
This goldstandard contains a bootstrapped lexicon of abusive words. The lexicon comprises a large set of English negative polar expressions annotated as either abusive or not.
Aug 19, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Kotnis, Bhushan, 2019, "KGE Algorithms", https://doi.org/10.11588/data/CSXYSS, heiDATA, V1
An updated method for link prediction that uses a regularization factor that models relation argument types Abstract (Kotnis and Nastase, 2017): Learning relations based on evidence from knowledge repositories relies on processing the available relation instances. Knowledge repos...
Nov 2, 2023 - Heidelberg Centre for Transcultural Studies (HCTS)
Henke, Konstantin; Arnold, Matthias, 2023, "Jing bao ground truth – text block crops and annotations", https://doi.org/10.11588/data/PVYWKB, heiDATA, V1
This is the data set related to the paper "Language Model Assisted OCR Classification for Republican Chinese Newspaper Text", JDADH 11/2023. In this work, we present methods to obtain a neural optical character recognition (OCR) tool for article blocks in a Republican Chinese new...
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