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        <title>MeSHup: Corpus for Full Text Biomedical Document Indexing</title>
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    <abstract>Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms. Currently, the vast number of biomedical articles in the PubMed database are manually annotated by human curators, which is time consuming and costly; therefore, a computational system that can assist the indexing is highly valuable. When developing supervised MeSH indexing systems, the availability of a large-scale annotated text corpus is desirable. A publicly available, large corpus that permits robust evaluation and comparison of various systems is important to the research community. We release a large scale annotated MeSH indexing corpus, MeSHup, which contains 1,342,667 full text articles, together with the associated MeSH labels and metadata, authors and publication venues that are collected from the MEDLINE database. We train an end-to-end model that combines features from documents and their associated labels on our corpus and report the new baseline.</abstract>
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%0 Conference Proceedings
%T MeSHup: Corpus for Full Text Biomedical Document Indexing
%A Wang, Xindi
%A Mercer, Robert E.
%A Rudzicz, Frank
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F wang-etal-2022-meshup
%X Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms. Currently, the vast number of biomedical articles in the PubMed database are manually annotated by human curators, which is time consuming and costly; therefore, a computational system that can assist the indexing is highly valuable. When developing supervised MeSH indexing systems, the availability of a large-scale annotated text corpus is desirable. A publicly available, large corpus that permits robust evaluation and comparison of various systems is important to the research community. We release a large scale annotated MeSH indexing corpus, MeSHup, which contains 1,342,667 full text articles, together with the associated MeSH labels and metadata, authors and publication venues that are collected from the MEDLINE database. We train an end-to-end model that combines features from documents and their associated labels on our corpus and report the new baseline.
%U https://aclanthology.org/2022.lrec-1.586
%P 5473-5483

Markdown (Informal)

[MeSHup: Corpus for Full Text Biomedical Document Indexing](https://aclanthology.org/2022.lrec-1.586) (Wang et al., LREC 2022)

• MeSHup: Corpus for Full Text Biomedical Document Indexing (Wang et al., LREC 2022)

ACL

• Xindi Wang, Robert E. Mercer, and Frank Rudzicz. 2022. MeSHup: Corpus for Full Text Biomedical Document Indexing. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5473–5483, Marseille, France. European Language Resources Association.