Identifying entities in text is an important step of semantic analysis. Some entity mentions comprise a name or description, but many include no information that identifies them in the system’s knowledge resources, which means that their identity cannot be established through traditional disambiguation. Consequently, such NIL (not in lexicon) entities have received little attention in entity linking systems and tasks so far. However, given the non-redundancy of knowledge on NIL entities, their lack of frequency priors, their potentially extreme ambiguity, and their numerousness, they constitute an important class of long-tail entities and pose a great challenge for state-of-the-art systems. In this paper, we describe a method for imputing identifying knowledge to NILs from generalized characteristics. We enrich the locally extracted information with profile models that rely on background knowledge in Wikidata. We describe and implement two profiling machines using state-of-the-art neural models. We evaluate their intrinsic behavior and their impact on the task of determining the identity of NIL entities.

识别文本中的实体是语义分析的重要步骤。一些实体提及包含名称或描述，但许多实体提及不包含在系统的知识资源中标识它们的信息，这意味着无法通过传统的歧义确定其身份。因此，到目前为止，这种NIL（不在词典中）实体在实体链接系统和任务中很少受到关注。但是，由于NIL实体知识的冗余，缺乏先验频率，潜在的极端歧义性以及数量众多，它们构成了重要的一类长尾实体，并且对状态实体提出了巨大的挑战。艺术系统。在本文中，我们描述了一种根据广义特征将识别知识推算为NIL的方法。我们使用依赖于Wikidata中背景知识的配置文件模型丰富了本地提取的信息。我们使用最先进的神经模型描述和实现两台配置文件机。我们评估它们的内在行为及其对确定NIL实体身份的任务的影响。