A Single-run Recognition of Nested Named Entities with Transformers

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. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.