Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes
Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes
复制标题
知识增强的生物医学命名实体识别和标准化：在蛋白质和基因中的应用
DOI：
10.1186/s12859-020-3375-3
复制
发表时间：
2020-01-30
影响因子：
3
通讯作者：
Lei, Bizun
中科院分区：
文献类型：
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作者：
Zhou, Huiwei;Ning, Shixian;Lei, Bizun
背景生物医学命名实体的自动识别和规范化是信息管理中许多下游应用的基础。然而，由于名称的变化和实体的模糊性，这项任务是具有挑战性的。一个生物医学实体可能有多个变体和一个变体可以表示几个不同的实体identifiers.ResultsTo补救上述问题，我们提出了一种新的知识增强系统的蛋白质/基因命名实体识别（PNER）和规范化（PNEN）。一方面，从生物医学知识库中提取大量的实体名称知识，用于识别更多的实体变体。另一方面，提取实体的结构知识并编码为标识符（ID）嵌入，然后将其用于更好的实体规范化。此外，由预训练的语言模型生成的深度上下文化单词表示也被纳入我们的知识增强系统中，用于对实体的多意义信息进行建模。BioCreative VI Bio-ID语料库上的实验结果表明，我们提出的知识增强系统分别达到0.871F1分数PNER和0.445F1分数PNEN，导致一个新的国家的最先进的performance.ConclusionsWe提出了一个知识增强的系统，结合实体知识和深上下文的单词表示。比较结果表明，实体知识是有益的PNER和PNEN任务，可以很好地结合上下文信息在我们的系统进一步改进。
BackgroundAutomated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information management. However, this task is challenging due to name variations and entity ambiguity. A biomedical entity may have multiple variants and a variant could denote several different entity identifiers.ResultsTo remedy the above issues, we present a novel knowledge-enhanced system for protein/gene named entity recognition (PNER) and normalization (PNEN). On one hand, a large amount of entity name knowledge extracted from biomedical knowledge bases is used to recognize more entity variants. On the other hand, structural knowledge of entities is extracted and encoded as identifier (ID) embeddings, which are then used for better entity normalization. Moreover, deep contextualized word representations generated by pre-trained language models are also incorporated into our knowledge-enhanced system for modeling multi-sense information of entities. Experimental results on the BioCreative VI Bio-ID corpus show that our proposed knowledge-enhanced system achieves 0.871F1-score for PNER and 0.445F1-score for PNEN, respectively, leading to a new state-of-the-art performance.ConclusionsWe propose a knowledge-enhanced system that combines both entity knowledge and deep contextualized word representations. Comparison results show that entity knowledge is beneficial to the PNER and PNEN task and can be well combined with contextualized information in our system for further improvement.