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。