Graph-Based Time Token Recognition using Graph Neural Networks and Stanza Library
| dc.contributor.author | Doms, Nicolas | |
| dc.contributor.author | Woernle, LisaHahn | |
| dc.contributor.editor | Wohlgemuth, Volker | |
| dc.contributor.editor | Kranzlmüller, Dieter | |
| dc.contributor.editor | Höb, Maximilian | |
| dc.date.accessioned | 2023-12-15T09:22:23Z | |
| dc.date.available | 2023-12-15T09:22:23Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | This publication aims to explore the capabilities of the Stanza NLP library on one hand and Graph Neural Networks on the other hand by combining them in a time token classification task. After providing information on the German ”WikiWarsDE” dataset that is used for training, the evaluation dataset consisting of environmental documents as well as the transformations applied to both datasets to enhance the model, the setup of the Neural Network is presented. Afterwards, some early test results are evaluated before possible enhancements to the model are suggested. | en |
| dc.identifier.doi | 10.18420/env2023-012 | |
| dc.identifier.isbn | 978-3-88579-736-4 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43332 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | EnviroInfo 2023 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-342 | |
| dc.subject | Graph Neural Network | |
| dc.subject | Entity Recognition | |
| dc.subject | Machine Learning | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Chronological Classification of Environmental Documents | |
| dc.title | Graph-Based Time Token Recognition using Graph Neural Networks and Stanza Library | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 142 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 135 | |
| gi.conference.date | 11.-13. Oktober 2023 | |
| gi.conference.location | Garching, Germany | |
| gi.conference.review | full | |
| gi.conference.sessiontitle | Innovative Approaches and Solutions |
Dateien
Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
- Name:
- GI_Proceedings_342_Digital_Paper_12.pdf
- Größe:
- 955.04 KB
- Format:
- Adobe Portable Document Format