@inproceedings{k-etal-2023-taxonomy,
title = "Taxonomy Expansion for Named Entity Recognition",
author = "K, Karthikeyan and
Vyas, Yogarshi and
Ma, Jie and
Paolini, Giovanni and
John, Neha and
Wang, Shuai and
Benajiba, Yassine and
Castelli, Vittorio and
Roth, Dan and
Ballesteros, Miguel",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.426",
doi = "10.18653/v1/2023.emnlp-main.426",
pages = "6895--6906",
abstract = "Training a Named Entity Recognition (NER) model often involves fixing a taxonomy of entity types. However, requirements evolve and we might need the NER model to recognize additional entity types. A simple approach is to re-annotate entire dataset with both existing and additional entity types and then train the model on the re-annotated dataset. However, this is an extremely laborious task. To remedy this, we propose a novel approach called Partial Label Model (PLM) that uses only partially annotated datasets. We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0.5 - 2.5 F1), including in novel settings for taxonomy expansion not considered in prior work. The gap between PLM and all other approaches is especially large in settings where there is limited data available for the additional entity types (as much as 11 F1), thus suggesting a more cost effective approaches to taxonomy expansion.",
}
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<abstract>Training a Named Entity Recognition (NER) model often involves fixing a taxonomy of entity types. However, requirements evolve and we might need the NER model to recognize additional entity types. A simple approach is to re-annotate entire dataset with both existing and additional entity types and then train the model on the re-annotated dataset. However, this is an extremely laborious task. To remedy this, we propose a novel approach called Partial Label Model (PLM) that uses only partially annotated datasets. We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0.5 - 2.5 F1), including in novel settings for taxonomy expansion not considered in prior work. The gap between PLM and all other approaches is especially large in settings where there is limited data available for the additional entity types (as much as 11 F1), thus suggesting a more cost effective approaches to taxonomy expansion.</abstract>
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%0 Conference Proceedings
%T Taxonomy Expansion for Named Entity Recognition
%A K, Karthikeyan
%A Vyas, Yogarshi
%A Ma, Jie
%A Paolini, Giovanni
%A John, Neha
%A Wang, Shuai
%A Benajiba, Yassine
%A Castelli, Vittorio
%A Roth, Dan
%A Ballesteros, Miguel
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F k-etal-2023-taxonomy
%X Training a Named Entity Recognition (NER) model often involves fixing a taxonomy of entity types. However, requirements evolve and we might need the NER model to recognize additional entity types. A simple approach is to re-annotate entire dataset with both existing and additional entity types and then train the model on the re-annotated dataset. However, this is an extremely laborious task. To remedy this, we propose a novel approach called Partial Label Model (PLM) that uses only partially annotated datasets. We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0.5 - 2.5 F1), including in novel settings for taxonomy expansion not considered in prior work. The gap between PLM and all other approaches is especially large in settings where there is limited data available for the additional entity types (as much as 11 F1), thus suggesting a more cost effective approaches to taxonomy expansion.
%R 10.18653/v1/2023.emnlp-main.426
%U https://aclanthology.org/2023.emnlp-main.426
%U https://doi.org/10.18653/v1/2023.emnlp-main.426
%P 6895-6906
Markdown (Informal)
[Taxonomy Expansion for Named Entity Recognition](https://aclanthology.org/2023.emnlp-main.426) (K et al., EMNLP 2023)
ACL
- Karthikeyan K, Yogarshi Vyas, Jie Ma, Giovanni Paolini, Neha John, Shuai Wang, Yassine Benajiba, Vittorio Castelli, Dan Roth, and Miguel Ballesteros. 2023. Taxonomy Expansion for Named Entity Recognition. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6895–6906, Singapore. Association for Computational Linguistics.