论文标题
GaussetExpander：一种简单的实体设置扩展的方法
GausSetExpander: A Simple Approach for Entity Set Expansion
论文作者
论文摘要
实体集扩展是一项重要的NLP任务，旨在将一小部分实体扩展到一个较大的实体，并从大量候选人中进行项目。在本文中，我们提出了GaussetExpander，这是一种基于最佳运输技术的无监督方法。我们建议将问题重新确定为选择最能完成种子集的实体。为此，我们将一个集合解释为具有质心的椭圆分布，代表以比例参数为代表的平均值和传播。最好的实体是增加套件传播最少的一个实体。我们通过与最先进的方法进行比较来证明我们的方法的有效性。
Entity Set Expansion is an important NLP task that aims at expanding a small set of entities into a larger one with items from a large pool of candidates. In this paper, we propose GausSetExpander, an unsupervised approach based on optimal transport techniques. We propose to re-frame the problem as choosing the entity that best completes the seed set. For this, we interpret a set as an elliptical distribution with a centroid which represents the mean and a spread that is represented by the scale parameter. The best entity is the one that increases the spread of the set the least. We demonstrate the validity of our approach by comparing to state-of-the art approaches.
