Extreme multi-label classification (XMC) is the problem of finding the
relevant labels for an input, from a very large universe of possible labels. We
consider XMC in the setting where labels are available only for groups of
samples - but not for individual ones. Current XMC approaches are not built for
such multi-instance multi-label (MIML) training data, and MIML approaches do
not scale to XMC sizes. We develop a new and scalable algorithm to impute
individual-sample labels from the group labels; this can be paired with any
existing XMC method to solve the aggregated label problem. We characterize the
statistical properties of our algorithm under mild assumptions, and provide a
new end-to-end framework for MIML as an extension. Experiments on both
aggregated label XMC and MIML tasks show the advantages over existing
approaches.

我们开发了一种新的可扩展算法，用于从组标签中填充单个样本标签，并提供了一个新的端对端框架作为扩展，解决多示例多标签问题，在聚合标签的 XMC 和 MIML 任务上的实验表明了对现有方法的优势。

来自聚合标签的极端多标记分类

Extreme Multi-label Classification from Aggregated Labels

Distant supervision for relation extraction is an efficient method to reduce
labor costs and has been widely used to seek novel relational facts in large
corpora, which can be identified as a multi-instance multi-label problem.
However, existing distant supervision methods suffer from selecting important
words in the sentence and extracting valid sentences in the bag. Towards this
end, we propose a novel approach to address these problems in this paper.
Firstly, we propose a linear attenuation simulation to reflect the importance
of words in the sentence with respect to the distances between entities and
words. Secondly, we propose a non-independent and identically distributed
(non-IID) relevance embedding to capture the relevance of sentences in the bag.
Our method can not only capture complex information of words about hidden
relations, but also express the mutual information of instances in the bag.
Extensive experiments on a benchmark dataset have well-validated the
effectiveness of the proposed method.

这篇研究提出了一种新方法来解决采用遥测监督的关系抽取中存在的问题，包括在句子中选择重要单词和提取有效句子等问题。该方法引入了线性衰减模拟和非独立同分布相关性嵌入两个新的概念来解决这些问题，在基准数据集上的实验表明，该方法能够有效地提取隐藏关系的复杂信息和实例间的相互信息。