21 to 30 of 121 Results
Feb 13, 2017
This dataverse contains PhD related material from the Faculty of Mathematics and Computer Science. |
Feb 14, 2017 - PhD related Material - Faculty of Mathematics and Computer Science
Merten, Thorsten, 2017, "Source Code, Data and Additional Material for the Thesis: "Identification of Software Features in Issue Tracking System Data"", https://doi.org/10.11588/data/10089, heiDATA, V2
This dataset provides the code and the data sets used in the PHD thesis "Identification of Software Features in Issue Tracking System Data" as well as the files that represent the results measured in experiments. For problem studies (e.g. chapters 10 and 11) the folders include t... |
May 22, 2017
Data publications of the 3D Spatial Data Processing Group at the Institute of Geography at Heidelberg University. |
Sep 11, 2017
Data publications from the Chair in Computer Assisted Clinical Medicine at the Medical Faculty Mannheim (Heidelberg University). |
Oct 10, 2017
Datenpublikationen des FID Altertumswissenschaften |
May 18, 2018
Data publications from the Institute of Pathology Mannheim at the Medical Faculty Mannheim (Heidelberg University). |
Jul 18, 2018
Datenpublikationen des Fachinformationsdienst Kunst - Fotografie - Design: arthistoricum.net |
Jan 31, 2019
Data publications from the DFG-funded research training group on Adaptive Information Processing from Heterogeneous Sources (AIPHES) at the CS Department at the Technical University of Darmstadt, the Institute for Computational Linguistics at the University of Heidelberg and the... |
Jan 31, 2019 - AIPHES
Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1
Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the... |
Feb 4, 2019 - AIPHES
Marasovic, Ana, 2019, "SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]", https://doi.org/10.11588/data/LWN9XE, heiDATA, V1
This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towar... |