In entity linking, mentions of named entities in raw text are disambiguated
against a knowledge base (KB). This work focuses on linking to unseen KBs that
do not have training data and whose schema is unknown during training. Our
approach relies on methods to flexibly convert entities from arbitrary KBs with
several attribute-value pairs into flat strings, which we use in conjunction
with state-of-the-art models for zero-shot linking. To improve the
generalization of our model, we use two regularization schemes based on
shuffling of entity attributes and handling of unseen attributes. Experiments
on English datasets where models are trained on the CoNLL dataset, and tested
on the TAC-KBP 2010 dataset show that our models outperform baseline models by
over 12 points of accuracy. Unlike prior work, our approach also allows for
seamlessly combining multiple training datasets. We test this ability by adding
both a completely different dataset (Wikia), as well as increasing amount of
training data from the TAC-KBP 2010 training set. Our models perform favorably
across the board.

本文研究实体链接在零样本链接中的应用，介绍了一种灵活的方法来将实体从任意的知识库中转化为平面字符串，与零样本链接模型配合使用，通过两种规则方法的改进，实现了模型的精度提升，实验结果表明我们的模型优于基准模型，并且本文的方法也可以无缝地结合多个训练数据集。

将实体与任意模式的未见知识库连接

Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas

This paper introduces a conceptually simple, scalable, and highly effective
BERT-based entity linking model, along with an extensive evaluation of its
accuracy-speed trade-off. We present a two-stage zero-shot linking algorithm,
where each entity is defined only by a short textual description. The first
stage does retrieval in a dense space defined by a bi-encoder that
independently embeds the mention context and the entity descriptions. Each
candidate is then re-ranked with a cross-encoder, that concatenates the mention
and entity text. Experiments demonstrate that this approach is state of the art
on recent zero-shot benchmarks (6 point absolute gains) and also on more
established non-zero-shot evaluations (e.g. TACKBP-2010), despite its relative
simplicity (e.g. no explicit entity embeddings or manually engineered mention
tables). We also show that bi-encoder linking is very fast with nearest
neighbour search (e.g. linking with 5.9 million candidates in 2 milliseconds),
and that much of the accuracy gain from the more expensive cross-encoder can be
transferred to the bi-encoder via knowledge distillation. Our code and models
are available at this https URL

本文介绍一种基于 BERT 的实体链接模型，采用两阶段的零样本链接算法，通过双编码器进行实体检索，并进一步用交叉编码器对候选实体进行重排序。经实验证明，这种方法不需要显式实体嵌入或手动工程化的实体表，已超越最近的零样本评估和已建立的非零样本评估（如 TACKBP-2010），并且具有速度快的优点。