论文标题
多标签少数/零射击学习，并从多个标签图中汇总知识
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
论文作者
论文摘要
很少有/零射击学习是许多分类任务的巨大挑战，在这些任务中，分类器需要识别几乎没有培训样本的课程实例。在多标签分类中，它变得更加困难，其中每个实例都用多个类标记。在本文中，我们提出了一个简单的多刻录聚合模型，该模型从编码不同的语义标签关系的多个标签图中融合了知识，以研究聚合知识如何使多标签零/少数弹奏文档分类受益。该模型利用三种语义信息，即预先训练的单词嵌入，标签描述和预定义的标签关系。在两个大型临床数据集（即模仿II和模拟III）和欧盟立法数据集中得出的实验结果表明，配备了多毛牌知识聚合的方法可在几乎所有/零发标签上的所有度量中都有显着的绩效提高。
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.
