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Artifacts using NLP Core Class Project (5)

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FastNER is a fast implementation of rule-based named entity recognition using hashed rule processing engine. There are two versions: FastNER supports token-based rules, FastCNER supports character-based rules.
Last Release on Mar 14, 2022
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina ...
Last Release on Feb 10, 2018
The library of EasyCIE.
Last Release on Feb 16, 2020
FastContext is an optimized Java implementation of ConText algorithm (https://www.ncbi.nlm.nih.gov/pubmed/23920642). It runs two orders of magnitude faster than previous two popular implementations: JavaConText and GeneralConText. Version 2.0 includes UIMA wrapper
Last Release on Mar 14, 2022
The commandline interface to the EasyCIE (Easy Clinical Information Extractor), which contains a complete end-to-end rule-based clinical NLP pipeline.
Last Release on Mar 14, 2022