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1 to 10 of 92 Results
Nov 22, 2019 - heiEDITIONS
Chinca, Mark; Hunter, Helen; Wolf, Jürgen; Young, Christopher, 2018, "Kaiserchronik - digital", https://doi.org/10.11588/data/PEFKJM, heiDATA, V3
The digital edition presents the entire manuscript transmission of the Kaiserchronik, both known and extant, in dual format: digital facsimiles of the manuscripts (where these are still extant) on the one hand, transcriptions of the manuscript text on the other. Manuscripts now l...
Nov 22, 2019 - Germania
Brozio, Jan Piet, 2019, "Zur absoluten Chronologie der Einzelgrabkultur in Norddeutschland und Nordjütland [Supplement]", https://doi.org/10.11588/data/HBAVWG, heiDATA, V2, UNF:6:PZt1V6obgH7b1f61ZaYOBA== [fileUNF]
Mit der Absicht ein typochronologisches Modell der geschweiften Becher der norddeutschen Einzelgrabkultur und damit eine detaillierte chronologische Differenzierung für die Diskussion historischer Prozesse zu entwickeln, erfolgt zunächst eine absolutchronologische Einordnung der...
Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Becker, Maria, 2019, "Genre-sensitive Neural Situation Entity classifier (DE, EN)", https://doi.org/10.11588/data/XXKWU0, heiDATA, V1
This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, synta...
Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Becker, Maria, 2019, "COREC – A neural multi-label COmmonsense RElation Classification system", https://doi.org/10.11588/data/E5EHBV, heiDATA, V1
We examine the learnability of Commonsense knowledge relations as represented in CONCEPTNET. We develop a neural open world multi-label classification system that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the spec...
Oct 8, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Ruppenhofer, Josef, 2019, "Affixoid Dataset (DE)", https://doi.org/10.11588/data/QKF4LT, heiDATA, V1, UNF:6:+MGK9lTPTXx7Rclu1BpPnw== [fileUNF]
The dataset contains the manual annotations for the COLING 2018 submission "Distinguishing affixoid formations from compounds" by Josef Ruppenhofer, Michael Wiegand, Rebecca Wilm and Katja Markert. 1788 complex words containing one of 7 German suffixoid candidates (e.g. -hai, -go...
Oct 7, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Marasović, Ana; Zhou, Mengfei; Frank, Anette, 2019, "The MSC Data Set", https://doi.org/10.11588/data/JEESIQ, heiDATA, V1
From this page you can download resources we created for modal sense classification as reported in Zhou et al. (2015), Marasović et al. (2016) and Marasović and Frank (2015) (see "Related Publication" below): Heuristically sense-annotated training data acquired from EUROPARL and...
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 5, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Wiegand, Michael; Bocionek, Christine; Ruppenhofer, Josef, 2019, "Sentiment Compound Data (DE)", https://doi.org/10.11588/data/LSTRK3, heiDATA, V1
This dataset contains gold standards that are required for building a classifier that automatically extracts opinion (noun) compounds.
Sep 5, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Wiegand, Michael; Ruppenhofer, Josef; Schulder, Marc, 2019, "Sentiment View Lexicon (EN)", https://doi.org/10.11588/data/2JK48O, heiDATA, V1
This gold standard contains sentiment expressions (verbs, nouns and adjectives) that have been annotated according to their (prior) sentiment view. Each sentiment expression is labelled either as actor or speaker view.
Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Wiegand, Michael, 2019, "GermEval-2018 Corpus (DE)", https://doi.org/10.11588/data/0B5VML, heiDATA, V1
This dataset comprises the training and test data (German tweets) from the GermEval 2018 Shared on Offensive Language Detection.
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