1 to 4 of 4 Results
Oct 7, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)
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... |
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines; Frank, Anette, 2023, "Head Selection Parsers and LSTM Labelers", https://doi.org/10.11588/data/BPWWJL, heiDATA, V1
This resource contains code, data and pre-trained models for various types of neural dependency parsers and LSTM labelers used in the papers: Do et al. (2017). "What Do We Need to Know About an Unknown Word When Parsing German" Do and Rehbein (2017). "Evaluating LSTM Models for G... |
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... |
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
Rehbein, Ines; Steen, Julius; Do, Bich-Ngoc; Frank, Anette, 2020, "Converter for content-to-head style syntactic dependencies", https://doi.org/10.11588/data/HE3BAZ, heiDATA, V1
A set of Python scripts that convert function-head style encodings in dependency treebanks in a content-head style encoding (as used in the UD treebanks) and vice versa (for adpositions, copula and coordination). For more information, see (Rehbein, Steen, Do & Frank 2017). |