ArticleJournal of cheminformatics2018
MER: a shell script and annotation server for minimal named entity recognition and linking.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- COVID-19 recommender system based on an annotated multilingual corpus.Genomics & informatics · 2021Article
- A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.JAMIA open · 2021Article
- External features enriched model for biomedical question answering.BMC bioinformatics · 2021Article
- Information Retrieval Using Machine Learning for Biomarker Curation in the Exposome-Explorer.Frontiers in research metrics and analytics · 2021Article
- Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics.Journal of medical Internet research · 2019Article
- Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm.Journal of cheminformatics · 2019Article
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Authors and funding
2 authors.
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Abstract
Named-entity recognition aims at identifying the fragments of text that mention entities of interest, that afterwards could be linked to a knowledge base where those entities are described. This manuscript presents our minimal named-entity recognition and linking tool (MER), designed with flexibility, autonomy and efficiency in mind. To annotate a given text, MER only requires: (1) a lexicon (text file) with the list of terms representing the entities of interest; (2) optionally a tab-separated values file with a link for each term; (3) and a Unix shell. Alternatively, the user can provide an ontology from where MER will automatically generate the lexicon and links files. The efficiency of MER derives from exploring the high performance and reliability of the text processing command-line tools grep and awk, and a novel inverted recognition technique. MER was deployed in a cloud infrastructure using multiple Virtual Machines to work as an annotation server and participate in the Technical Interoperability and Performance of annotation Servers task of BioCreative V.5. The results show that our solution processed each document (text retrieval and annotation) in less than 3 s on average without using any type of cache. MER was also compared to a state-of-the-art dictionary lookup solution obtaining competitive results not only in computational performance but also in precision and recall. MER is publicly available in a GitHub repository ( https://github.com/lasigeBioTM/MER ) and through a RESTful Web service ( http://labs.fc.ul.pt/mer/ ).
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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.