ArticleDatabase : the journal of biological databases and curation2018
Wide-scope biomedical named entity recognition and normalization with CRFs, fuzzy matching and character level modeling.
Article in Database : the journal of biological databases and curation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Chemical entity normalization for successful translational development of Alzheimer's disease and dementia therapeutics.Journal of biomedical semantics · 2024Article
- Gilda: biomedical entity text normalization with machine-learned disambiguation as a service.Bioinformatics advances · 2022Article
- Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes.BMC bioinformatics · 2020Article
- Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies.Frontiers in genetics · 2020Article
- Construction, evaluation, and application of an electronic medical record corpus for cerebral palsy rehabilitation.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a system for automatically identifying a multitude of biomedical entities from the literature. This work is based on our previous efforts in the BioCreative VI: Interactive Bio-ID Assignment shared task in which our system demonstrated state-of-the-art performance with the highest achieved results in named entity recognition. In this paper we describe the original conditional random field-based system used in the shared task as well as experiments conducted since, including better hyperparameter tuning and character level modeling, which led to further performance improvements. For normalizing the mentions into unique identifiers we use fuzzy character n-gram matching. The normalization approach has also been improved with a better abbreviation resolution method and stricter guideline compliance resulting in vastly improved results for various entity types. All tools and models used for both named entity recognition and normalization are publicly available under open license.Database URL: https://github.com/TurkuNLP/BioCreativeVI_BioID_assignment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.