Named Entity Recognition for Specific Domains - Take Advantage of Transfer Learning
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
Automated text analysis as named entity recognition (NER) heavily relies on large amounts of high-quality training data. For domain-specific NER transfer learning approaches aim to overcome the problem of lacking domain-specific training data. In this paper, we investigate transfer learning approaches in order to improve domain-specific NER in low-ressource domains. The first part of the paper is dedicated to information transfer from known to unknown entities using BiLSTM-CRF neural networks, considering also the influence of varying training data size. In the second part instead, pre-trained BERT models are fine-tuned to domain-specific German NER. The performance of models of both architectures is compared w.r.t. different hyperparameters and a set of 16 entities. The experiments are based on the revised German SmartData Corpus, and a baseline model, trained on this corpus.
Details
Originalsprache | Englisch |
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Seiten (von - bis) | 4-15 |
Seitenumfang | 12 |
Fachzeitschrift | International Journal of Information Science and Technology |
Jahrgang | 6 |
Ausgabenummer | 3 |
Publikationsstatus | Veröffentlicht - 2022 |
Peer-Review-Status | Ja |
Externe IDs
ORCID | /0000-0001-9756-6390/work/142250121 |
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Schlagworte
Schlagwörter
- Automated text analysis, NER