61 to 70 of 184 Results
Sep 5, 2019 -
Sentiment Compound Data (DE)
Plain Text - 18.2 KB -
MD5: 4a17ffc27c9f3b240fbf4fe17783c89c
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Mar 26, 2020
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
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 -
MACE-AL-TREE
ZIP Archive - 141.9 KB -
MD5: 7de327971177c2124d8f388a19b1c4c6
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Mar 26, 2020 -
MACE-AL
ZIP Archive - 326.8 KB -
MD5: 056a7e70a8f8b6e8fa72e3eead763d39
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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 -
The MSC Data Set
ZIP Archive - 6.2 MB -
MD5: 98dbe1d608c24c3dfd31f166daeee77b
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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... |
Aug 19, 2019
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... |
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Fankhauser, Peter; Do, Bich-Ngoc; Kupietz, Marc, 2023, "Neural Dependency Parser with Biaffine Attention", https://doi.org/10.11588/data/DZ9MUS, heiDATA, V1
This resource contains the code of the dependency parser used in the paper: Fankhauser, et al. (2020). "Evaluating a Dependency Parser on DeReKo". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017). In addition, we include two pre-trai... |