11 to 20 of 55 Results
Jul 15, 2019
Nastase, Vivi; Fritz, Devon; Frank, Anette, 2019, "DeModify", https://doi.org/10.11588/data/KIWEMF, heiDATA, V1
deModify consists of 3631 instances, each with three annotations obtained through CrowdFlower. An instance is a short story in which a modifier is annotated with respect to its impact on the information in the story, assessed through its deletion from the context: crucial, not-cr... |
Jul 15, 2019 -
DeModify
Tab-Separated Values - 112.2 KB -
MD5: 9859efc83ee0b6a30af19448be4d6f0b
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Jul 15, 2019 -
DeModify
Tab-Separated Values - 5.1 MB -
MD5: 12bab5c05a384c4fbe64c9afd81f9c6d
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Oct 22, 2019
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... |
Dec 10, 2019
Becker, Maria, 2019, "GER_SET: Situation Entity Type labelled corpus for German", https://doi.org/10.11588/data/BBQYD0, heiDATA, V1
Semantic clause types, also called Situation Entity (SE) types (Smith, 2003) are linguistic characterizations of aspectual properties shown to be useful for tasks like argumentation structure analysis (Becker et al., 2016), genre characterization (Palmer and Friedrich, 2014), and... |
Sep 2, 2019 -
Opinion role extractor
ZIP Archive - 20.8 MB -
MD5: 6704c06c5a8566eb05c3a8e0e0baebc2
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Sep 2, 2019
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. |
Sep 2, 2019 -
GermEval-2018 Corpus (DE)
ZIP Archive - 14.8 MB -
MD5: 6471a35acf802906383e6d19e5241b37
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Sep 5, 2019 -
Sentiment Compound Data (DE)
Plain Text - 34.6 KB -
MD5: 13ac9f60aa9ba2fbb42d0b9d2b9f6e2f
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Aug 19, 2019
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... |