@inproceedings{singh-etal-2018-structured,
title = "Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding",
author = "Singh, Gaurav and
Thomas, James and
Marshall, Iain and
Shawe-Taylor, John and
Wallace, Byron C.",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1308",
doi = "10.18653/v1/D18-1308",
pages = "2837--2842",
abstract = "We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts.",
}
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%0 Conference Proceedings
%T Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
%A Singh, Gaurav
%A Thomas, James
%A Marshall, Iain
%A Shawe-Taylor, John
%A Wallace, Byron C.
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F singh-etal-2018-structured
%X We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts.
%R 10.18653/v1/D18-1308
%U https://aclanthology.org/D18-1308
%U https://doi.org/10.18653/v1/D18-1308
%P 2837-2842
Markdown (Informal)
[Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding](https://aclanthology.org/D18-1308) (Singh et al., EMNLP 2018)
ACL