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

2018/10/02 by Singh, Gaurav, Thomas, James, Marshall, Iain J. +2
#FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1810.01468

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. In our experiments the proposed method outperforms state-of-the-art approaches on the important task of automatically assigning MeSH terms to biomedical abstracts.

Related

• Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization
• Sequence Labeling: A Practical Approach
• Fine-Tuning Pretrained Language Models With Label Attention for Biomedical Text Classification
• Parallel sequence tagging for concept recognition
• Semi-supervised sequence tagging with bidirectional language models