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Extracting concepts from biomedical texts is a key to support many advanced applications such as biomedical information retrieval. However, in clinical notes Named Entity Recognition (NER) has to deal with various types of errors such as spelling errors, grammatical errors, truncated sentences, and non-standard abbreviations. Moreover, in numerous countries, NER is challenged by the availability of many resources originally developed and only suitable for English texts. This paper presents the Cimind system, a multilingual system dedicated to named entity recognition in medical texts based on a phonetic similarity measure.
This article was published in the following journal.
Name: Journal of biomedical informatics
Disease named entity recognition (NER) plays an important role in biomedical research. There are a significant number of challenging issues to be addressed; among these, the identification of rare dis...
The Publisher regrets that, due to a typesetting mistake, it has now become necessary to make the following corrections: All phonetic transcriptions (between square brackets and slashes) should be cor...
Clinical named entity recognition aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and cr...
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