Entity linking is the task of identifying mentions of entities in text, and
linking them to entries in a knowledge base. This task is especially difficult
in microblogs, as there is little additional text to provide disambiguating
context; rather, authors rely on an implicit common ground of shared knowledge
with their readers. In this paper, we attempt to capture some of this implicit
context by exploiting the social network structure in microblogs. We build on
the theory of homophily, which implies that socially linked individuals share
interests, and are therefore likely to mention the same sorts of entities. We
implement this idea by encoding authors, mentions, and entities in a continuous
vector space, which is constructed so that socially-connected authors have
similar vector representations. These vectors are incorporated into a neural
structured prediction model, which captures structural constraints that are
inherent in the entity linking task. Together, these design decisions yield F1
improvements of 1%-5% on benchmark datasets, as compared to the previous
state-of-the-art.

本文试图通过利用微博中的社交网络结构，将作者、提及和实体编码成一个连续向量空间，并将这些向量纳入神经结构预测模型中，以捕捉实体链接任务中固有的结构约束。与现有的最新技术相比，这些设计决策在基准数据集上的 F1 值提高了 1%-5%。