Bidirectional LSTM-CNNs with Extended Features for Named Entity Recognition

BÖLÜCÜ N., Akgol D., Tuc S.

International Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT), İstanbul, Türkiye, 24 - 26 Nisan 2019 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/ebbt.2019.8741631
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Named Entity Recognition (NER) is vital preprocessing step for many Natural Language Processing applications such as relation extraction and question answering. NER has been studied for decades. While, much of the earlier studies for NER have focused on using powerful features and knowledge resources, recent studies using Deep Learning techniques have not needed to use these powerful features and knowledge resources. Instead, approach of these studies is learning powerful features from the data by itself.