@inproceedings{ion-etal-2019-racais,
title = "{RACAI}{'}s System at {P}harma{C}o{NER} 2019",
author = "Ion, Radu and
P{\u{a}}i{\textcommabelow{s}}, Vasile Florian and
Mitrofan, Maria",
editor = "Jin-Dong, Kim and
Claire, N{\'e}dellec and
Robert, Bossy and
Louise, Del{\'e}ger",
booktitle = "Proceedings of the 5th Workshop on BioNLP Open Shared Tasks",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5714",
doi = "10.18653/v1/D19-5714",
pages = "90--99",
abstract = "This paper describes the Named Entity Recognition system of the Institute for Artificial Intelligence {``}Mihai Dr{\u{a}}g{\u{a}}nescu{''} of the Romanian Academy (RACAI for short). Our best F1 score of 0.84984 was achieved using an ensemble of two systems: a gazetteer-based baseline and a RNN-based NER system, developed specially for PharmaCoNER 2019. We will describe the individual systems and the ensemble algorithm, compare the final system to the current state of the art, as well as discuss our results with respect to the quality of the training data and its annotation strategy. The resulting NER system is language independent, provided that language-dependent resources and preprocessing tools exist, such as tokenizers and POS taggers.",
}
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<abstract>This paper describes the Named Entity Recognition system of the Institute for Artificial Intelligence “Mihai Drăgănescu” of the Romanian Academy (RACAI for short). Our best F1 score of 0.84984 was achieved using an ensemble of two systems: a gazetteer-based baseline and a RNN-based NER system, developed specially for PharmaCoNER 2019. We will describe the individual systems and the ensemble algorithm, compare the final system to the current state of the art, as well as discuss our results with respect to the quality of the training data and its annotation strategy. The resulting NER system is language independent, provided that language-dependent resources and preprocessing tools exist, such as tokenizers and POS taggers.</abstract>
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%0 Conference Proceedings
%T RACAI’s System at PharmaCoNER 2019
%A Ion, Radu
%A Păi\textcommabelows, Vasile Florian
%A Mitrofan, Maria
%Y Jin-Dong, Kim
%Y Claire, Nédellec
%Y Robert, Bossy
%Y Louise, Deléger
%S Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ion-etal-2019-racais
%X This paper describes the Named Entity Recognition system of the Institute for Artificial Intelligence “Mihai Drăgănescu” of the Romanian Academy (RACAI for short). Our best F1 score of 0.84984 was achieved using an ensemble of two systems: a gazetteer-based baseline and a RNN-based NER system, developed specially for PharmaCoNER 2019. We will describe the individual systems and the ensemble algorithm, compare the final system to the current state of the art, as well as discuss our results with respect to the quality of the training data and its annotation strategy. The resulting NER system is language independent, provided that language-dependent resources and preprocessing tools exist, such as tokenizers and POS taggers.
%R 10.18653/v1/D19-5714
%U https://aclanthology.org/D19-5714
%U https://doi.org/10.18653/v1/D19-5714
%P 90-99
Markdown (Informal)
[RACAI’s System at PharmaCoNER 2019](https://aclanthology.org/D19-5714) (Ion et al., BioNLP 2019)
ACL
- Radu Ion, Vasile Florian Păiș, and Maria Mitrofan. 2019. RACAI’s System at PharmaCoNER 2019. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 90–99, Hong Kong, China. Association for Computational Linguistics.