Abstrakt
In the article we present a single-run approach to recognizing nested named entities using neural networks with transformers. The main advantage of this approach is that a single model is trained to recognize all entity types.
The model can identify all entities in a single pass. Our main contribution is the simplified representation of nested named entities on the token level, and evaluation of the presented approach on three languages - Polish (PolEval 2018 dataset), German (GermEval 2014 dataset), and Czech (CNEC 2.0 dataset).
For each dataset we obtained state-of-the-art results. For Polish and German we obtained a significant improvement - 1.4 pp and 3.0 pp respectively.
For Czech we obtained an improvement of 0.5 pp.