Entity embeddings, which represent different aspects of each entity with a
single vector like word embeddings, are a key component of neural entity
linking models. Existing entity embeddings are learned from canonical Wikipedia
articles and local contexts surrounding target entities. Such entity embeddings
are effective, but too distinctive for linking models to learn contextual
commonality. We propose a simple yet effective method, FGS2EE, to inject
fine-grained semantic information into entity embeddings to reduce the
distinctiveness and facilitate the learning of contextual commonality. FGS2EE
first uses the embeddings of semantic type words to generate semantic
embeddings, and then combines them with existing entity embeddings through
linear aggregation. Extensive experiments show the effectiveness of such
embeddings. Based on our entity embeddings, we achieved new sate-of-the-art
performance on entity linking.

文章提出了简单且有效的 FGS2EE 方法，通过将语义类型词的嵌入与现有实体嵌入进行线性聚合，注入了细粒度的语义信息进入实体嵌入，以减少其鲜明性并便于上下文语境学习。实验证明，这种嵌入方法在实体链接任务中表现优于先前的方法，并取得了新的最佳效果。

通过语义加强实体嵌入来提高实体链接

Improving Entity Linking through Semantic Reinforced Entity Embeddings

Neural entity linking models are very powerful, but run the risk of
overfitting to the domain they are trained in. For this problem, a domain is
characterized not just by genre of text but even by factors as specific as the
particular distribution of entities, as neural models tend to overfit by
memorizing properties of frequent entities in a dataset. We tackle the problem
of building robust entity linking models that generalize effectively and do not
rely on labeled entity linking data with a specific entity distribution. Rather
than predicting entities directly, our approach models fine-grained entity
properties, which can help disambiguate between even closely related entities.
We derive a large inventory of types (tens of thousands) from Wikipedia
categories, and use hyperlinked mentions in Wikipedia to distantly label data
and train an entity typing model. At test time, we classify a mention with this
typing model and use soft type predictions to link the mention to the most
similar candidate entity. We evaluate our entity linking system on the
CoNLL-YAGO dataset (Hoffart et al., 2011) and show that our approach
outperforms prior domain-independent entity linking systems. We also test our
approach in a harder setting derived from the WikilinksNED dataset (Eshel et
al., 2017) where all the mention-entity pairs are unseen during test time.
Results indicate that our approach generalizes better than a state-of-the-art
neural model on the dataset.

该论文介绍了一种可以构建鲁棒实体链接模型的方法，该方法将注意力放在建立细粒度实体属性的模型上，具有更好的泛化性能，使用维基百科类别的大型库存来远程标记数据并训练实体类型模型，使用软类型预测将提及链接到最相似的候选实体上。

领域无关实体链接的细粒度实体类型划分

Fine-Grained Entity Typing for Domain Independent Entity Linking

Cross-lingual entity linking maps an entity mention in a source language to
its corresponding entry in a structured knowledge base that is in a different
(target) language. While previous work relies heavily on bilingual lexical
resources to bridge the gap between the source and the target languages, these
resources are scarce or unavailable for many low-resource languages. To address
this problem, we investigate zero-shot cross-lingual entity linking, in which
we assume no bilingual lexical resources are available in the source
low-resource language. Specifically, we propose pivot-based entity linking,
which leverages information from a high-resource "pivot" language to train
character-level neural entity linking models that are transferred to the source
low-resource language in a zero-shot manner. With experiments on 9 low-resource
languages and transfer through a total of 54 languages, we show that our
proposed pivot-based framework improves entity linking accuracy 17% (absolute)
on average over the baseline systems, for the zero-shot scenario. Further, we
also investigate the use of language-universal phonological representations
which improves average accuracy (absolute) by 36% when transferring between
languages that use different scripts.

本研究探讨了零资源语言的跨语言实体链接问题，利用高资源语言建立了基于字级别神经实体链接模型的枢轴式实体链接框架，实现了跨 54 种语言的零 - shot transfer，并在 9 种低资源语言上进行了实验，相比于基线系统，平均提高了 17% 的实体链接准确性。同时，文章还探讨了语言通用音韵表示的使用，在不同语言之间转移时将平均准确率提高了 36%。