Clustering-based Inference for Zero-Shot Biomedical Entity Linking
Clustering-based Inference for Zero-Shot Biomedical Entity Linking
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发表时间：
2020-10
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通讯作者：
Rico Angell;Nicholas Monath;Sunil Mohan;Nishant Yadav;A. McCallum
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作者：
Rico Angell;Nicholas Monath;Sunil Mohan;Nishant Yadav;A. McCallum
由于生物医学知识库中的实体数量巨大，只有一小部分实体具有相应的标记训练数据。这需要一个零样本实体链接模型，该模型能够使用学习到的实体表示来链接未见过的实体的提及。然而，现有的零样本实体链接模型独立链接每个提及，忽略实体提及之间的文档间/文档内关系。这些关系对于链接生物医学文本中的提及非常有用，在生物医学文本中，由于提及具有通用或高度专业化的形式，因此链接决策通常很困难。在本文中，我们介绍了一种模型，在该模型中，链接决策不仅可以通过链接到知识库实体来做出，还可以通过聚类将多个提及分组在一起并联合进行链接预测来做出。在最大的公开生物医学数据集的实验中，我们将零样本实体链接的最佳独立预测精度提高了 2.5 个百分点，并且我们的联合推理模型进一步将实体链接提高了 1.8 个百分点。
Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates a zero-shot entity linking model which is able to link mentions of unseen entities using learned representations of entities. Existing zero-shot entity linking models however link each mention independently, ignoring the inter/intra-document relationships between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a KB entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 2.5 points of accuracy, and our joint inference model further improves entity linking by 1.8 points.