{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/structured-multi-label-biomedical-text","title":"Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding","arxiv_id":"1810.01468","date":"2018-10-02","proceeding":"EMNLP 2018 10","authors":["Gaurav Singh","James Thomas","Iain J. Marshall","John Shawe-Taylor","Byron C. Wallace"],"abstract":"We propose a model for tagging unstructured texts with an arbitrary number of\nterms drawn from a tree-structured vocabulary (i.e., an ontology). We treat\nthis as a special case of sequence-to-sequence learning in which the decoder\nbegins at the root node of an ontological tree and recursively elects to expand\nchild nodes as a function of the input text, the current node, and the latent\ndecoder state. In our experiments the proposed method outperforms\nstate-of-the-art approaches on the important task of automatically assigning\nMeSH terms to biomedical abstracts.","url_abs":"http://arxiv.org/abs/1810.01468v1","url_pdf":"http://arxiv.org/pdf/1810.01468v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"structured-multi-label-biomedical-text","repo_url":"https://github.com/gauravsc/NTD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}