Institute of Formal and Applied Linguistics
Charles University, Czech Republic
Faculty of Mathematics and Physics
NameTag 3
1. Introduction
NameTag 3 is an open-source tool for both flat and nested named entity recognition (NER). NameTag 3 identifies proper names in text and classifies them into a set of predefined categories, such as names of persons, locations, organizations, etc.
NameTag 3 offers state-of-the-art or near state-of-the-art performance in English, German, Spanish, Dutch, Czech and Ukrainian.
NameTag is available in the following versions:
- NameTag 3:
- NER tool for both flat and nested named entity recognition,
- fine-tuned LLM with either a softmax classification head for flat NER or a seq2seq decoding head for nested NER,
- NameTag 3 is available as a commandline Python script, as a web application, and NameTag REST Web Service hosted by LINDAT/CLARIN.
- NameTag 2:
- NER tool,
- uses frozen BERT contextualized word embeddings with LSTM contextualization layer,
- recognizes both flat and nested named entities of arbitrary depth, depending on the trained model,
- NameTag 2 is available as a web application NameTag Web Application, and NameTag REST Web Service hosted by LINDAT/CLARIN.
- NameTag 1:
- fast and robust NER tool,
- feedforward neural network,
- recognizes flat named entities,
- available as commandline tool, C++ source code, with bindings in Perl, Python and C#,
- NameTag 1 is available as a web application NameTag Web Application, and NameTag REST Web Service hosted by LINDAT/CLARIN.
NameTag 3 is a free software under Mozilla Public License 2.0, and the linguistic models are free for non-commercial use and distributed under CC BY-NC-SA license, although for some models the original data used to create the model may impose additional licensing conditions. NameTag is versioned using Semantic Versioning.
Copyright 2024 Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Czech Republic.
2. Current Release
NameTag 3 can be used either as a commandline tool or by requesting the NameTag webservice:
- LINDAT/CLARIN hosts the NameTag Web Application.
- LINDAT/CLARIN also hosts the NameTag REST Web Service.
NameTag 3 source code can be found at GitHub.
3. Models
The individual models are described on the NameTag 3 Models webpage and are distributed via the LINDAT repository.
4. Results at a Glance
5. License
NameTag 3 is a free software under Mozilla Public License 2.0, and the linguistic models are free for non-commercial use and distributed under CC BY-NC-SA license, although for some models the original data used to create the model may impose additional licensing conditions. NameTag is versioned using Semantic Versioning.
The associated models and data are licensed under CC BY-NC-SA, although for some models the original data used to create the model may impose additional licensing conditions.
If you use this tool for scientific work, please give us credit by referencing Straková et al. (2019) (see BibTeX for referencing).
6. Acknowledgements
Acknowledgements for the individual language models are listed in NameTag 3 Models page.
This work has been supported by the MŠMT OP JAK program, project No. CZ.02.01.01/00/22_008/0004605 and by the Grant Agency of the Czech Republic under the EXPRO program as project “LUSyD” (project No. GX20-16819X). The work described herein has also been using data provided by the [LINDAT/CLARIAH-CZ Research Infrastructure https://lindat.cz], supported by the Ministry of Education, Youth and Sports of the Czech Republic (Project No. LM2023062).
6.1. Publications
Straková Jana, Straka Milan, Hajič Jan: Neural Architectures for Nested NER through Linearization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Copyright © Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-950737-48-2, pp. 5326-5331, 2019.
@inproceedings{strakova-etal-2019-neural,
title = "Neural Architectures for Nested {NER} through Linearization",
author = "Strakov{\'a}, Jana and
Straka, Milan and
Hajic, Jan",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1527",
doi = "10.18653/v1/P19-1527",
pages = "5326--5331",
}
7. Contact
Authors:
