BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text.,Bioinformatics

当前位置： X-MOL 学术 › Bioinformatics › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text.
Bioinformatics ( IF 4.4 ) Pub Date : 2020-09-25 , DOI: 10.1093/bioinformatics/btaa837
Ronghui You ₁ , Yuxuan Liu ₁ , Hiroshi Mamitsuka _{2,

3} , Shanfeng Zhu _{1,

4,

5,

6}

Affiliation

With the rapid increase of biomedical articles, large-scale automatic Medical Subject Headings (MeSH) indexing has become increasingly important. FullMeSH, the only method for large-scale MeSH indexing with full text, suffers from three major drawbacks: FullMeSH 1) uses Learning To Rank (LTR), which is time-consuming, 2) can capture some pre-defined sections only in full text, and 3) ignores the whole MEDLINE database.

中文翻译：

BERTMeSH：具有全文本的大规模高性能MeSH索引的深度上下文表示学习。

随着生物医学文章的迅速增加，大规模的自动医学主题词（MeSH）索引已变得越来越重要。FullMeSH是用全文本进行大规模MeSH索引的唯一方法，它具有三个主要缺点：FullMeSH 1）使用费时的学习排名（LTR），2）仅可以完全捕获一些预定义的部分文本，以及3）忽略整个MEDLINE数据库。

更新日期：2020-09-25

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11