Accurate entity linkers have been produced for domains and languages where
annotated data (i.e., texts linked to a knowledge base) is available. However,
little progress has been made for the settings where no or very limited amounts
of labeled data are present (e.g., legal or most scientific domains). In this
work, we show how we can learn to link mentions without having any labeled
examples, only a knowledge base and a collection of unannotated texts from the
corresponding domain. In order to achieve this, we frame the task as a
multi-instance learning problem and rely on surface matching to create initial
noisy labels. As the learning signal is weak and our surrogate labels are
noisy, we introduce a noise detection component in our model: it lets the model
detect and disregard examples which are likely to be noisy. Our method, jointly
learning to detect noise and link entities, greatly outperforms the surface
matching baseline. For a subset of entity categories, it even approaches the
performance of supervised learning.

本文介绍了一种在没有标记数据的情况下，通过多实例学习和噪声检测的方法，学习从知识库中链接提及的实体，特别是对于科学领域等标记信息有限的领域。