41 to 50 of 185 Results
Sep 5, 2019 -
Sentiment Compound Data (DE)
Plain Text - 31.9 KB -
MD5: bf369686743f258705fd6cc675cfcaf0
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Sep 5, 2019 -
Sentiment Compound Data (DE)
Adobe PDF - 126.2 KB -
MD5: 846a2849d5f0f4a119d504d79260c6fa
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Oct 7, 2019
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... |
Oct 7, 2019 -
Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]
ZIP Archive - 3.0 MB -
MD5: 63c05670056bb1992a1e5ec370f0ccf3
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Oct 7, 2019
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 -
The MSC Data Set
ZIP Archive - 6.2 MB -
MD5: 98dbe1d608c24c3dfd31f166daeee77b
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Oct 8, 2019
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 8, 2019 -
Affixoid Dataset (DE)
Tabular Data - 61.6 KB - 1 Variables, 1787 Observations - UNF:6:+MGK9lTPTXx7Rclu1BpPnw==
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Oct 8, 2019 -
Affixoid Dataset (DE)
Plain Text - 758 B -
MD5: 017f60a9c77782cd97a45c4dd74e117c
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Oct 22, 2019
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... |