Work

Chemical Entity Recognition for MEDLINE Indexing.

Year: 2020

Type: article

Abstract: Chemical entity recognition is essential for indexing scientific literature in the MEDLINE database at the National Library of Medicine. However, the tool currently used to suggest terms for indexing, the Medical Text Indexer, was not originally conceived as a chemical recognition tool. It has instead been adapted to the task via its use of MetaMap and the addition of in-house patterns and rules. In order to develop a tool more suitable for chemical recognition, we have created a collection of 200 MEDLINE titles and abstracts annotated with genes, proteins, inorganic and organic chemicals, as well as other biological molecules. We use this collection to evaluate eleven chemical entity recognition systems, where we seek to identify a tool that effectively recognizes chemical entities for indexing and also performs well on chemical recognition beyond the indexing task. We observe the highest performance with a SciBERT ensemble.

Source: PubMed

Authors: Max E Savery, Willie J Rogers, Malvika Pillai, James G Mork, Dina Demner-Fushman

Institutions: National Institutes of Health, United States National Library of Medicine

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

Open Access status: green

OpenAlex ID: W3030678071

FWCI: 0.291

Cites: 28

Cited by: 5

Related to: 10