Neural Architectures for Named Entity Recognition

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures—one based on bidirectional LSTMs and conditional random fields, and the other that… CONTINUE READING

7 Figures & Tables

Topics

Statistics

0100200300201620172018
Citations per Year

566 Citations

Semantic Scholar estimates that this publication has 566 citations based on the available data.

See our FAQ for additional information.