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1 to 10 of 20 Results
Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Nastase, Vivi; Kotnis, Bhushan, 2019, "Abstract graphs, abstract paths, grounded paths for Freebase and NELL", https://doi.org/10.11588/data/AVLFPZ, heiDATA, V1
We describe a method for representing knowledge graphs that capture an intensional representation of the original extensional information. This representation is very compact, and it abstracts away from individual links, allowing us to find better path candidates, as shown by the...
Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Nastase, Vivi; Hitschler, Julian, 2019, "ACL word segmentation correction", https://doi.org/10.11588/data/VK99LU, heiDATA, V1
The data in this collection consists of two parallel directories, one ("raw") containing the raw text of 18850 articles from the ACL 2013/02 collection, the other ("re-segmented") the word-resegmented version of these articles, obtained using nematus, a seq2seq neural model used...
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...
Jul 12, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
Opitz, Juri, 2019, "AMR parse quality prediction [Source Code]", https://doi.org/10.11588/data/STHBGW, heiDATA, V1
Accuracy prediction for AMR parsing predicts 33 accuracy metrics for a given sentence and its (automatic) AMR parse Abstract (Opitz and Frank, 2019): Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition o...
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...
Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
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...
Empirical Linguistics and Computational Language Modeling (LiMo)(Department of Computational Linguistics of Heidelberg University and Leibniz Institute for the German Language)
Empirical Linguistics and Computational Language Modeling (LiMo) logo
Jul 12, 2019
Data publications of the Leibniz ScienceCampus “Empirical Linguistics and Computational Language Modeling” The Leibniz ScienceCampus “Empirical Linguistics and Computational Language Modeling” (LiMo) is a cooperative research project between the Leibniz Institute for the German L...
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...
Dec 10, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
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 - 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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