@inproceedings{weber-etal-2023-wrf,
title = "{WRF}: Weighted Rouge-F1 Metric for Entity Recognition",
author = "Weber, Lukas and
Jothi Ramalingam, Krishnan and
Beyer, Matthias and
Zimmermann, Axel",
editor = {Deutsch, Daniel and
Dror, Rotem and
Eger, Steffen and
Gao, Yang and
Leiter, Christoph and
Opitz, Juri and
R{\"u}ckl{\'e}, Andreas},
booktitle = "Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems",
month = nov,
year = "2023",
address = "Bali, Indonesia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eval4nlp-1.1",
doi = "10.18653/v1/2023.eval4nlp-1.1",
pages = "1--11",
abstract = "The continuous progress in Named Entity Recognition allows the identification of complex entities in multiple domains. The traditionally used metrics like precision, recall, and F1-score can only reflect the classification quality of the underlying NER model to a limited extent. Existing metrics do not distinguish between a non-recognition of an entity and a misclassification of an entity. Additionally, the dealing with redundant entities remains unaddressed. We propose WRF, a Weighted Rouge F1 metric for Entity Recognition, to solve the mentioned gaps in currently available metrics. We successfully employ the WRF metric for automotive entity recognition, followed by a comprehensive qualitative and quantitative analysis of the obtained results.",
}
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<abstract>The continuous progress in Named Entity Recognition allows the identification of complex entities in multiple domains. The traditionally used metrics like precision, recall, and F1-score can only reflect the classification quality of the underlying NER model to a limited extent. Existing metrics do not distinguish between a non-recognition of an entity and a misclassification of an entity. Additionally, the dealing with redundant entities remains unaddressed. We propose WRF, a Weighted Rouge F1 metric for Entity Recognition, to solve the mentioned gaps in currently available metrics. We successfully employ the WRF metric for automotive entity recognition, followed by a comprehensive qualitative and quantitative analysis of the obtained results.</abstract>
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%0 Conference Proceedings
%T WRF: Weighted Rouge-F1 Metric for Entity Recognition
%A Weber, Lukas
%A Jothi Ramalingam, Krishnan
%A Beyer, Matthias
%A Zimmermann, Axel
%Y Deutsch, Daniel
%Y Dror, Rotem
%Y Eger, Steffen
%Y Gao, Yang
%Y Leiter, Christoph
%Y Opitz, Juri
%Y Rücklé, Andreas
%S Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems
%D 2023
%8 November
%I Association for Computational Linguistics
%C Bali, Indonesia
%F weber-etal-2023-wrf
%X The continuous progress in Named Entity Recognition allows the identification of complex entities in multiple domains. The traditionally used metrics like precision, recall, and F1-score can only reflect the classification quality of the underlying NER model to a limited extent. Existing metrics do not distinguish between a non-recognition of an entity and a misclassification of an entity. Additionally, the dealing with redundant entities remains unaddressed. We propose WRF, a Weighted Rouge F1 metric for Entity Recognition, to solve the mentioned gaps in currently available metrics. We successfully employ the WRF metric for automotive entity recognition, followed by a comprehensive qualitative and quantitative analysis of the obtained results.
%R 10.18653/v1/2023.eval4nlp-1.1
%U https://aclanthology.org/2023.eval4nlp-1.1
%U https://doi.org/10.18653/v1/2023.eval4nlp-1.1
%P 1-11
Markdown (Informal)
[WRF: Weighted Rouge-F1 Metric for Entity Recognition](https://aclanthology.org/2023.eval4nlp-1.1) (Weber et al., Eval4NLP-WS 2023)
ACL
- Lukas Weber, Krishnan Jothi Ramalingam, Matthias Beyer, and Axel Zimmermann. 2023. WRF: Weighted Rouge-F1 Metric for Entity Recognition. In Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems, pages 1–11, Bali, Indonesia. Association for Computational Linguistics.