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://aclanthology.org/D18-1308
- DOI:
- 10.18653/v1/D18-1308
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D18-1308.pdf
- Code
- gauravsc/NTD