Semiautomatic data generation for academic Named Entity Recognition in German text corpora
- An NER model is trained to recognize three types of entities in academic contexts: person, organization, and research area. Training data is generated semiautomatically from newspaper articles with the help of word lists for the individual entity types, an off-the-shelf NE recognizer, and an LLM. Experiments fine-tuning a BERT model with different strategies of post-processing the automatically generated data result in several NER models achieving overall F1 scores of up to 92.45%.
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| Author: | Pia SchwarzORCiD |
|---|---|
| URN: | urn:nbn:de:bsz:mh39-128423 |
| URL: | https://aclanthology.org/2024.konvens-main.20 |
| Parent Title (English): | Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024). September 10-13, 2024 |
| Publisher: | Association for Computational Linguistics |
| Place of publication: | Wien |
| Editor: | Pedro Henrique Luz de Araujo, Andreas Baumann, Dagmar Gromann, Brigitte Krenn, Benjamin Roth, Michael Wiegand |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2024 |
| Date of Publication (online): | 2024/10/07 |
| Publishing Institution: | Leibniz-Institut für Deutsche Sprache (IDS) |
| Publicationstate: | Veröffentlichungsversion |
| Reviewstate: | Peer-Review |
| Tag: | BERT model; academic Named Entity Recognition; data generation; named entity; text corpora |
| GND Keyword: | Computerlinguistik; Deutsch; Großes Sprachmodell; Korpus <Linguistik>; Named Entity Recognition |
| First Page: | 173 |
| Last Page: | 181 |
| DDC classes: | 400 Sprache / 400 Sprache, Linguistik |
| Open Access?: | ja |
| Linguistics-Classification: | Computerlinguistik |
| Linguistics-Classification: | Korpuslinguistik |
| Program areas: | Digitale Sprachwissenschaft |
| Licence (English): |  Creative Commons - Attribution 4.0 International |