Year: 2021
Type: conference-paper
Abstract: 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.
Source: Procedia Computer Science
Authors: Michał Marcińczuk, Jarema Radom
Institution: Wrocław University of Science and Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G1888088267, G4211762235
OpenAlex ID: W3204380607