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. In our experiments the proposed method outperforms state-of-the-art approaches on the important task of automatically assigning MeSH terms to biomedical abstracts.
@article{arxiv.1810.01468,
title = {Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding},
author = {Gaurav Singh and James Thomas and Iain J. Marshall and John Shawe-Taylor and Byron C. Wallace},
journal= {arXiv preprint arXiv:1810.01468},
year = {2018}
}