Few/Zero-shot learning is a big challenge of many classifications tasks,
where a classifier is required to recognise instances of classes that have very
few or even no training samples. It becomes more difficult in multi-label
classification, where each instance is labelled with more than one class. In
this paper, we present a simple multi-graph aggregation model that fuses
knowledge from multiple label graphs encoding different semantic label
relationships in order to study how the aggregated knowledge can benefit
multi-label zero/few-shot document classification. The model utilises three
kinds of semantic information, i.e., the pre-trained word embeddings, label
description, and pre-defined label relations. Experimental results derived on
two large clinical datasets (i.e., MIMIC-II and MIMIC-III) and the EU
legislation dataset show that methods equipped with the multi-graph knowledge
aggregation achieve significant performance improvement across almost all the
measures on few/zero-shot labels.

这篇研究提出了一种基于多种语义信息的多图聚合模型以增强多标签零 / 少样本文档分类效果，并在多个数据集上进行了实验验证。