Machine learningDeep learning / NLP / CV

Transferno učenje sa prepoznavanjem imenovanih entiteta

Transferno učenje sa prepoznavanjem imenovanih entiteta (NER) prilagođava veliki unapred trenirani jezički model — kao što je BERT, RoBERTa, ili enkoder specifičan za domen — zadatku identifikacije i klasifikacije imenovanih entiteta (osoba, lokacija, organizacija, datuma, itd.) u tekstu. Ponovnom upotrebom bogatih lingvističkih reprezentacija naučenih iz masivnih korpusa, ovaj pristup zahteva samo skromnu količinu obeleženih NER podataka, dok postiže najsavremeniju tačnost detekcije i klasifikacije raspona.

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Izvori

  1. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics. DOI: 10.18653/v1/N19-1423
  2. Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Transfer Learning with Named Entity Recognition (Pretrained Encoder Fine-Tuned for NER). ScholarGate. https://scholargate.app/sr/deep-learning/transfer-learning-with-named-entity-recognition

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Citirana u

ScholarGateTransfer Learning with Named Entity Recognition (Transfer Learning with Named Entity Recognition (Pretrained Encoder Fine-Tuned for NER)). Preuzeto 2026-06-15 sa https://scholargate.app/sr/deep-learning/transfer-learning-with-named-entity-recognition · Skup podataka: https://doi.org/10.5281/zenodo.20539026