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Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding

Gaurav Singh, James Thomas, Iain Marshall, John Shawe-Taylor, Byron C. Wallace


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.
Anthology ID:
D18-1308
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2837–2842
Language:
URL:
https://aclanthology.org/D18-1308
DOI:
10.18653/v1/D18-1308
Bibkey:
Cite (ACL):
Gaurav Singh, James Thomas, Iain Marshall, John Shawe-Taylor, and Byron C. Wallace. 2018. Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2837–2842, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding (Singh et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1308.pdf
Attachment:
 D18-1308.Attachment.pdf
Video:
 https://aclanthology.org/D18-1308.mp4
Code
 gauravsc/NTD