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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2837–2842
Language:
URL:
https://www.aclweb.org/anthology/D18-1308
DOI:
10.18653/v1/D18-1308
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http://aclanthology.lst.uni-saarland.de/D18-1308.pdf
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Video:
 https://vimeo.com/306056257