11 to 20 of 93 Results
Jun 16, 2014 - Statistical Natural Language Processing Group
Sokolov, Artem; Jehl Laura; Hieber Felix; Ruppert, Eugen; Riezler, Stefan, 2014, "BoostCLIR: JP-EN Relevance Marked Patent Corpus", https://doi.org/10.11588/data/10001, heiDATA, V1
BoostCLIR is a bilingual (Japanese-English) corpus of patent abstracts, extracted from the MAREC patent data, and the data from the NTCIR PatentMT workshop collections, accompanied with relevance judgements for the task of patent prior-art search. Important: The English side of t... |
Feb 6, 2019 - AIPHES
Heinzerling, Benjamin, 2019, "BPEmb: Pre-trained Subword Embeddings in 275 Languages (LREC 2018)", https://doi.org/10.11588/data/V9CXPR, heiDATA, V1
BPEmb is a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE). In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively, and for some languages better than alternative subword approaches, while r... |
Jul 31, 2020 - Cluster of Excellence - Asia and Europe in a Global Context
Arnold, Matthias; Dober, Agnes, 2020, "Cataloging Cultural Objects (CCO) – The CCO Commons examples in VRA Core 4 XML", https://doi.org/10.11588/data/KKTC9G, heiDATA, V1
“Cataloging Cultural Objects - a Guide to Describing Cultural Works and Their Images” (CCO) provides a data content standard for catalogers of cultural heritage. It is a guidebook for how to populate data elements and where to apply controlled vocabulary standards. The guide is f... |
Jul 20, 2023 - arthistoricum.net@heiDATA
Pattee, Aaron, 2023, "CITADEL: Computational Investigation of the Topographical and Architectural Designs in an Evolving Landscape (Research Data)", https://doi.org/10.11588/data/ZDOC7O, heiDATA, V1
The data found in this repository contain the basis for the historical, architectural, and geo-spatial analyses discussed in the dissertation entitled: CITADEL – Computation Investigation of the Topographical and Architectural Designs in an Evolving Landscape. These data include... |
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 24, 2023 - HITS MBM
Rennekamp, Benedikt; Gräter, Frauke, 2023, "Collagen breaks at weak sacrificial bonds taming its mechanoradicals [Data]", https://doi.org/10.11588/data/HJ6SVM, heiDATA, V1, UNF:6:Da6omTKjrSvNcDwyJxuHOg== [fileUNF]
This dataset contains input files for MD simulations, derived breakage counts from these simulations that are used to generate the figures in the publication, and the experimental, uncropped SDS-PAGE gels of the presented results in the related publication. Abstract of related pu... |
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". |