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Project Group: edu.utah.bmi.nlp

NLP Core Class Project

 edu.utah.bmi.nlp : nlp-core

The core classes for easycie

Last Version: 1.4.1.5-jdk1.8

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FastNER (Fast Name Entity Recognition)

 edu.utah.bmi.nlp : fastner

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 Version: 1.4.1.5-jdk1.8

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SectionDetectorR (Rule-Based Sectionetector)

 edu.utah.bmi.nlp : section-detector-r

SectionDetectorR is a Rule-Based SectionDetector leveraging FastNER for section header detection.

Last Version: 1.4.1.5-jdk1.8

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FastContext (An optimized implementation of ConText algorithm)

 edu.utah.bmi.nlp : fastcontext

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 Version: 1.4.1.5-jdk1.8

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EasyCIE

 edu.utah.bmi.nlp : easycie

The mega repository that contains all easycie related NLP components. Starting from 1.4.0.8, the projects are reorganized under a parent project for maven dependency management. The last digit of a version id is and will be used to indicate which version of JDK is used to compile the jars. For instance, ".8" uses JDK 8, and ".11" uses JDK 11, etc.

Last Version: 1.4.1.5-jdk1.8

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EasyCIE(Easy Clinical Information Extractor-console)

 edu.utah.bmi.nlp : easycie-console

The commandline interface to the EasyCIE (Easy Clinical Information Extractor), which contains a complete end-to-end rule-based clinical NLP pipeline.

Last Version: 1.4.1.5-jdk1.8

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EasyCIE Project

 edu.utah.bmi.nlp : easycie-gui

The library of EasyCIE.

Last Version: 3.0.2

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RuSH (Rule-based sentence Segmenter using Hashing)

 edu.utah.bmi.nlp : rush

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 M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616 This version allows defining section scopes for sentence segmentation.

Last Version: 3.0

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