Term Extraction for Domain Modeling
| dc.contributor.author | Kruse, Theresa | |
| dc.contributor.author | Lohr, Dominic | |
| dc.contributor.author | Berges, Marc | |
| dc.contributor.author | Kohlhase, Michael | |
| dc.contributor.author | Moghbeli, Halimeh | |
| dc.contributor.author | Schütz, Marcel | |
| dc.contributor.editor | Schulz, Sandra | |
| dc.contributor.editor | Kiesler, Natalie | |
| dc.date.accessioned | 2024-09-03T16:26:19Z | |
| dc.date.available | 2024-09-03T16:26:19Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Adaptive learning systems need to use domain and learner models to provide meaningful support for learners. Building fine-grained domain models by hand is very time-consuming, so the demand for partial automation is high. This paper investigates how term extraction tools can support constructing a domain model. Therefore, we study if different automatic term extraction tools give comparable results to a human annotator. Our results show that the current extraction tools support the process, but their results are not directly usable and still need human adjustments. | en |
| dc.identifier.doi | 10.18420/delfi2024_33 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/44516 | |
| dc.language.iso | en | |
| dc.pubPlace | Bonn | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Proceedings of DELFI 2024 | |
| dc.relation.ispartofseries | DELFI | |
| dc.subject | domain modeling | |
| dc.subject | term extraction | |
| dc.subject | adaptive learning system | |
| dc.subject | programming | |
| dc.title | Term Extraction for Domain Modeling | en |
| dc.type | Text/Conference paper | |
| mci.conference.date | 09.-11. September 2024 | |
| mci.conference.location | Fulda | |
| mci.conference.sessiontitle | KI Einsatz in der Hochschule II | |
| mci.document.quality | digidoc | |
| mci.reference.pages | 369-377 |
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