@inproceedings{zhang-etal-2020-automatic,
title = "Automatic Term Name Generation for Gene Ontology: Task and Dataset",
author = "Zhang, Yanjian and
Chen, Qin and
Zhang, Yiteng and
Wei, Zhongyu and
Gao, Yixu and
Peng, Jiajie and
Huang, Zengfeng and
Sun, Weijian and
Huang, Xuanjing",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.422",
doi = "10.18653/v1/2020.findings-emnlp.422",
pages = "4705--4710",
abstract = "Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine. Most previous research focuses on inferring new GO terms, while the term names that reflect the gene function are still named by the experts. To fill this gap, we propose a novel task, namely term name generation for GO, and build a large-scale benchmark dataset. Furthermore, we present a graph-based generative model that incorporates the relations between genes, words and terms for term name generation, which exhibits great advantages over the strong baselines.",
}
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<abstract>Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine. Most previous research focuses on inferring new GO terms, while the term names that reflect the gene function are still named by the experts. To fill this gap, we propose a novel task, namely term name generation for GO, and build a large-scale benchmark dataset. Furthermore, we present a graph-based generative model that incorporates the relations between genes, words and terms for term name generation, which exhibits great advantages over the strong baselines.</abstract>
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%0 Conference Proceedings
%T Automatic Term Name Generation for Gene Ontology: Task and Dataset
%A Zhang, Yanjian
%A Chen, Qin
%A Zhang, Yiteng
%A Wei, Zhongyu
%A Gao, Yixu
%A Peng, Jiajie
%A Huang, Zengfeng
%A Sun, Weijian
%A Huang, Xuanjing
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-automatic
%X Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine. Most previous research focuses on inferring new GO terms, while the term names that reflect the gene function are still named by the experts. To fill this gap, we propose a novel task, namely term name generation for GO, and build a large-scale benchmark dataset. Furthermore, we present a graph-based generative model that incorporates the relations between genes, words and terms for term name generation, which exhibits great advantages over the strong baselines.
%R 10.18653/v1/2020.findings-emnlp.422
%U https://aclanthology.org/2020.findings-emnlp.422
%U https://doi.org/10.18653/v1/2020.findings-emnlp.422
%P 4705-4710
Markdown (Informal)
[Automatic Term Name Generation for Gene Ontology: Task and Dataset](https://aclanthology.org/2020.findings-emnlp.422) (Zhang et al., Findings 2020)
ACL
- Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, and Xuanjing Huang. 2020. Automatic Term Name Generation for Gene Ontology: Task and Dataset. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4705–4710, Online. Association for Computational Linguistics.