EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text

arXiv:2304.07805v2 Announce Type: replace-cross
Abstract: Background Medical research generates millions of publications and it is a great challenge for researchers to utilize this information in full since its scale and complexity greatly surpasses human reading capabilities. Automated text mining can help extract and connect information spread across this large body of literature but this technology is not easily accessible to life scientists. Results Here, we developed an easy-to-use end-to-end pipeline for deep learning- and dictionary-based named entity recognition (NER) of …

abstract arxiv automated capabilities challenge complexity cs.cl deep learning dictionary easy extract human information medical medical research mining pipeline publications q-bio.qm reading recognition research researchers scale text type