11 to 20 of 64 Results
Feb 26, 2024 - RATIO_EXPLAIN
Becker, Maria, 2024, "CO-NNECT", https://doi.org/10.11588/data/SAJAD3, heiDATA, V1
This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classificatio... |
Feb 26, 2024 - RATIO_EXPLAIN
Becker, Maria, 2024, "CoCo-Ex", https://doi.org/10.11588/data/K8MCIW, heiDATA, V1
CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. |
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). |
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... |
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines, 2023, "Datasets for Dependency Tree Reranking", https://doi.org/10.11588/data/E5NOYH, heiDATA, V1
This resource contains the datasets for dependency tree reranking in 3 languages: English, German and Czech. The creation, analysis and experiment results of the datasets are described in the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation". |
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... |
Oct 26, 2020arthistoricum.net@heiDATA
Open Research Data from the German Center for Art History (Deutsches Forum für Kunstgeschichte) |
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... |
Jan 23, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
Daza, Angel, 2020, "Encoder-Decoder Model for Semantic Role Labeling", https://doi.org/10.11588/data/TOI9NQ, heiDATA, V1
Abstract (Daza & Frank 2019): We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need paral... |
Jun 7, 2023 - IWR Computer Graphics
Mara, Hubert, 2023, "ErKon3D - Quantifying Deformation in Aegean Sealing Practices [Dataset]", https://doi.org/10.11588/data/UMJXI0, heiDATA, V1
In Bronze Aegean society, seals played an important role by authenticating, securing and marking. The study of the seals and their engraved motifs provides valuable insight into the social and political organization and administration of Aegean societies. A key research question... |