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131 to 140 of 214 Results
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...
Dec 20, 2021 - Germania
Meier, Thomas, 2021, "Methodenprobleme einer Chronologie der Merowingerzeit in Süddeutschland. Eine Diskussion anhand von Matthias Friedrich „Archäologische Chronologie und historische Interpretation: Die Merowingerzeit in Süddeutschland“ (2016) [Supplement]", https://doi.org/10.11588/data/M5PH4W, heiDATA, V2
Drei Tabellen zur Relativchronologie der Merowingerzeit und ihrer Phasengliederung anhand von Typen von Grabfunden.
Jul 18, 2018 - Propylaeum@heiDATA
Küster, Hansjörg, 2018, "Mittelalterliche Pflanzenreste von der Marktstätte und Brotlaube in Konstanz [Dataset]", https://doi.org/10.11588/data/ARWJHB, heiDATA, V1
During excavations in medieval Konstanz very many samples were taken for archaeobotanical analyses. Samples could be dated to different time layers from High to Late Middle Ages. Spelt, millet and oats were probably the most important cereals. But also flax, hemp, figs and spices...
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...
Sep 20, 2017 - Practical Theology
Karcher, Stefan, 2017, "Nachlass Heinrich Bassermann", https://doi.org/10.11588/data/PFZXUD, heiDATA, V1
Die vorliegende Datenbank ergänzt das abgekürzte Nachlassverzeichnis der Schriften und Autographen im Nachlass Heinrich Bassermanns (UB HEID. HS. 3895 – Nachlass Heinrich Bassermann) und dient als detailliertes Findmittel. Wichtigste Funktion des Nachlass- und Predigtverzeichniss...
Nov 5, 2020 - Bericht der Römisch-Germanischen Kommission
Regenye, Judit, 2020, "Narratives for Lengyel funerary practice (Supplementary Material)", https://doi.org/10.11588/data/2EVBVW, heiDATA, V1, UNF:6:MxTchY/kk0cK40hJKw0Jjw== [fileUNF]
Supplementary tables 1–6; supplementary figures 1–3
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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