@inproceedings{wang-meng-2018-bacteria,
title = "Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model",
author = "Wang, Qiuyue and
Meng, Xiaofeng",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2317",
doi = "10.18653/v1/W18-2317",
pages = "147--150",
abstract = "Automatic recognition of biomedical entities in text is the crucial initial step in biomedical text mining. In this pa-per, we investigate employing modern neural network models for recognizing biomedical entities. To compensate for the small amount of training data in biomedical domain, we propose to integrate dictionaries into the neural model. Our experiments on BB3 data sets demonstrate that state-of-the-art neural network model is promising in recognizing biomedical entities even with very little training data. When integrated with dictionaries, its performance could be greatly improved, achieving the competitive performance compared with the best dictionary-based system on the entities with specific terminology, and much higher performance on the entities with more general terminology.",
}
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%0 Conference Proceedings
%T Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model
%A Wang, Qiuyue
%A Meng, Xiaofeng
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the BioNLP 2018 workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F wang-meng-2018-bacteria
%X Automatic recognition of biomedical entities in text is the crucial initial step in biomedical text mining. In this pa-per, we investigate employing modern neural network models for recognizing biomedical entities. To compensate for the small amount of training data in biomedical domain, we propose to integrate dictionaries into the neural model. Our experiments on BB3 data sets demonstrate that state-of-the-art neural network model is promising in recognizing biomedical entities even with very little training data. When integrated with dictionaries, its performance could be greatly improved, achieving the competitive performance compared with the best dictionary-based system on the entities with specific terminology, and much higher performance on the entities with more general terminology.
%R 10.18653/v1/W18-2317
%U https://aclanthology.org/W18-2317
%U https://doi.org/10.18653/v1/W18-2317
%P 147-150
Markdown (Informal)
[Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model](https://aclanthology.org/W18-2317) (Wang & Meng, BioNLP 2018)
ACL