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Empirical Linguistics and Computational Language Modeling (LiMo) (Department of Computational Linguistics of Heidelberg University and Leibniz Institute for the German Language)
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1 to 10 of 55 Results
Jul 15, 2019
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...
Gzip Archive - 2.5 GB - MD5: dc2a64fb2d88cccf5e62d9400cbca1af
Data
Gzip Archive - 204.7 MB - MD5: 6dfafe4e7d5b29a882b59f43ec9eb4ae
Data
Jul 15, 2019
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...
Gzip Archive - 371.1 MB - MD5: 96d089771cde56bb9ac5296189fb403b
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text files
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...
Jul 12, 2019
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
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...
ZIP Archive - 6.2 KB - MD5: 04927f554601f39dd7e2d86a5e62d681
Code
Oct 8, 2019 - Affixoid Dataset (DE)
Tab-Delimited - 61.6 KB - MD5: 8e2e107227a8ab7d59fb9a48dfa9f475
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