Name
Abstract | ||
|---|---|---|
Automated text analysis as named entity recognition (NER) heavily relies on large amounts of high-quality training data. Transfer learning approaches aim to overcome the problem of lacking domain-specific training data. In this paper, we investigate different transfer learning approaches to recognize unknown domain-specific entities, including the influence on varying training data size. The experiments are based on the revised German SmartData Corpus, and a baseline model, trained on this corpus. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CiSt49399.2021.9357262 | 2020 6th IEEE Congress on Information Science and Technology (CiSt) |
Keywords | DocType | ISSN |
Transfer Learning,Named Entity Recognition,German,domain-specific,BiLSTM-CRF | Conference | 2327-185X |
ISBN | Citations | PageRank |
978-1-7281-6647-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sunna Torge | 1 | 0 | 0.34 |
| Waldemar Hahn | 2 | 0 | 0.34 |
| René Jäkel | 3 | 40 | 5.28 |