@inproceedings{stanislawek-etal-2019-named,
title = "Named Entity Recognition - Is There a Glass Ceiling?",
author = "Stanislawek, Tomasz and
Wr{\'o}blewska, Anna and
W{\'o}jcicka, Alicja and
Ziembicki, Daniel and
Biecek, Przemyslaw",
editor = "Bansal, Mohit and
Villavicencio, Aline",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1058",
doi = "10.18653/v1/K19-1058",
pages = "624--633",
abstract = "Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study illustrates weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, training process, and for checking model quality and stability.",
}
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<abstract>Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study illustrates weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, training process, and for checking model quality and stability.</abstract>
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%0 Conference Proceedings
%T Named Entity Recognition - Is There a Glass Ceiling?
%A Stanislawek, Tomasz
%A Wróblewska, Anna
%A Wójcicka, Alicja
%A Ziembicki, Daniel
%A Biecek, Przemyslaw
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F stanislawek-etal-2019-named
%X Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study illustrates weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, training process, and for checking model quality and stability.
%R 10.18653/v1/K19-1058
%U https://aclanthology.org/K19-1058
%U https://doi.org/10.18653/v1/K19-1058
%P 624-633
Markdown (Informal)
[Named Entity Recognition - Is There a Glass Ceiling?](https://aclanthology.org/K19-1058) (Stanislawek et al., CoNLL 2019)
ACL
- Tomasz Stanislawek, Anna Wróblewska, Alicja Wójcicka, Daniel Ziembicki, and Przemyslaw Biecek. 2019. Named Entity Recognition - Is There a Glass Ceiling?. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 624–633, Hong Kong, China. Association for Computational Linguistics.