LATTE: Latent Type Modeling for Biomedical Entity Linking
LATTE: Latent Type Modeling for Biomedical Entity Linking
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DOI：
10.1609/aaai.v34i05.6526
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发表时间：
2019-11
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影响因子：
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通讯作者：
Ming Zhu;B. Celikkaya;Parminder Bhatia;Chandan K. Reddy
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作者：
Ming Zhu;B. Celikkaya;Parminder Bhatia;Chandan K. Reddy
实体链接是将自然语言文本中提到的命名实体链接到策划知识库中的实体的任务。这在生物医学领域非常重要，它可以用于对大量临床记录和生物医学文献进行语义注释，并在诸如统一医学语言系统（UMLS）之类的本体中描述标准化概念。我们观察到，有了精确的类型信息，实体消歧就变成了一项简单的任务。然而，在生物医学领域通常无法获得细粒度的类型信息。因此，我们提出了一个潜在类型实体链接模型LATTE，该模型通过建模关于提及和实体的潜在细粒度类型信息来改进实体链接。与之前在提及和实体之间直接进行实体链接的方法不同，LATTE在没有直接监督的情况下联合进行实体消歧和潜在细粒度类型学习。我们在两个生物医学数据集上评估了我们的模型：MedMentions，一个用UMLS概念注释的大型公共数据集，以及一个用ICD概念注释的去识别的医生笔记语库。广泛的实验评估表明，我们的模型比几种最先进的技术实现了显着的性能改进。
Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS). We observe that with precise type information, entity disambiguation becomes a straightforward task. However, fine-grained type information is usually not available in biomedical domain. Thus, we propose LATTE, a LATent Type Entity Linking model, that improves entity linking by modeling the latent fine-grained type information about mentions and entities. Unlike previous methods that perform entity linking directly between the mentions and the entities, LATTE jointly does entity disambiguation, and latent fine-grained type learning, without direct supervision. We evaluate our model on two biomedical datasets: MedMentions, a large scale public dataset annotated with UMLS concepts, and a de-identified corpus of dictated doctor's notes that has been annotated with ICD concepts. Extensive experimental evaluation shows our model achieves significant performance improvements over several state-of-the-art techniques.