Previous work has shown promising results in performing entity linking by
measuring not only the affinities between mentions and entities but also those
amongst mentions. In this paper, we present novel training and inference
procedures that fully utilize mention-to-mention affinities by building minimum
arborescences (i.e., directed spanning trees) over mentions and entities across
documents in order to make linking decisions. We also show that this method
gracefully extends to entity discovery, enabling the clustering of mentions
that do not have an associated entity in the knowledge base. We evaluate our
approach on the Zero-Shot Entity Linking dataset and MedMentions, the largest
publicly available biomedical dataset, and show significant improvements in
performance for both entity linking and discovery compared to identically
parameterized models. We further show significant efficiency improvements with
only a small loss in accuracy over previous work, which use more
computationally expensive models.

本文提出了一种基于最小有向树的新型训练和推断程序，从而完全利用提及之间的亲和关系来进行实体链接和发现，并在 Zero-Shot Entity Linking 数据集和 MedMentions 数据集中展示出显著的性能提升。

基于树形结构的监督聚类方法实现实体链接和发现

Entity Linking and Discovery via Arborescence-based Supervised  Clustering

When working with any sort of knowledge base (KB) one has to make sure it is
as complete and also as up-to-date as possible. Both tasks are non-trivial as
they require recall-oriented efforts to determine which entities and
relationships are missing from the KB. As such they require a significant
amount of labor. Tables on the Web, on the other hand, are abundant and have
the distinct potential to assist with these tasks. In particular, we can
leverage the content in such tables to discover new entities, properties, and
relationships. Because web tables typically only contain raw textual content we
first need to determine which cells refer to which known entities---a task we
dub table-to-KB matching. This first task aims to infer table semantics by
linking table cells and heading columns to elements of a KB. Then second task
builds upon these linked entities and properties to not only identify novel
ones in the same table but also to bootstrap their type and additional
relationships. We refer to this process as novel entity discovery and, to the
best of our knowledge, it is the first endeavor on mining the unlinked cells in
web tables. Our method identifies not only out-of-KB (``novel'') information
but also novel aliases for in-KB (``known'') entities. When evaluated using
three purpose-built test collections, we find that our proposed approaches
obtain a marked improvement in terms of precision over our baselines whilst
keeping recall stable.

这篇研究提出一种方法，通过挖掘网络表格中的信息来发现知识库中尚未链接的实体和关系，从而补充和完善知识库。研究结果表明，该方法有效提高了精度并保持回收率稳定。

从 Web 表格中发现新实体

Novel Entity Discovery from Web Tables

Corpus-based set expansion (i.e., finding the "complete" set of entities
belonging to the same semantic class, based on a given corpus and a tiny set of
seeds) is a critical task in knowledge discovery. It may facilitate numerous
downstream applications, such as information extraction, taxonomy induction,
question answering, and web search. To discover new entities in an expanded
set, previous approaches either make one-time entity ranking based on
distributional similarity, or resort to iterative pattern-based bootstrapping.
The core challenge for these methods is how to deal with noisy context features
derived from free-text corpora, which may lead to entity intrusion and semantic
drifting. In this study, we propose a novel framework, SetExpan, which tackles
this problem, with two techniques: (1) a context feature selection method that
selects clean context features for calculating entity-entity distributional
similarity, and (2) a ranking-based unsupervised ensemble method for expanding
entity set based on denoised context features. Experiments on three datasets
show that SetExpan is robust and outperforms previous state-of-the-art methods
in terms of mean average precision.

本文提出了一种基于语料库的集合扩展方法，通过选择干净的上下文特征计算实体分布相似度和基于去噪上下文特征的排序无监督集成方法，该方法在三个数据集上的实验证明是健壮的且优于之前的最新方法。