Physical Sciences and Mathematics Commons^™

Chrono: A System For Normalizing Temporal Expressions, Amy L. Olex, Luke G. Maffey, Nicholas Morton, Bridget T. Mcinnes

Computer Science Publications

The Chrono System: Chrono is a hybrid rule-based and machine learning system written in Python and built from the ground up to identify temporal expressions in text and normalizes them into the SCATE schema. Input text is preprocessed using Python’s NLTK package, and is run through each of the four primary modules highlighted here. Note that Chrono does not remove stopwords because they add temporal information and context, and Chrono does not tokenize sentences. Output is an Anafora XML file with annotated SCATE entities. After minor parsing logic adjustments, Chrono has emerged as the top performing system for SemEval 2018 …

Parsing Metamap Files In Hadoop, Amy Olex, Alberto Cano, Bridget T. Mcinnes

Computer Science Publications

The UMLS::Association CUICollector module identifies UMLS Concept Unique Identifier bigrams and their frequencies in a biomedical text corpus. CUICollector was re-implemented in Hadoop MapReduce to improve algorithm speed, flexibility, and scalability. Evaluation of the Hadoop implementation compared to the serial module produced equivalent results and achieved a 28x speedup on a single-node Hadoop system.

R Code To Accompany “Principal Component Analysis And Optimization: A Tutorial”, Robert Reris, J. Paul Brooks

Statistical Sciences and Operations Research Data

This data accompanies "Principal Component Analysis and Optimization: A Tutorial" by Robert Reris and J. Paul Brooks, presented at the 2015 INFORMS Computing Society Conference, Operations Research and Computing: Algorithms and Software for Analytics, Richmond, Virginia January 11-13, 2015.

The data contains R code, output, and comments that follow the examples for principal component analysis in the paper.

Data Files To Accompany "The Support Vector Machine And Mixed Integer Linear Programming: Ramp Loss Svm With L1-Norm Regularization", Eric J. Hess, J. Paul Brooks

Statistical Sciences and Operations Research Data

These files accompany, "The Support Vector Machine and Mixed Integer Linear Programming: Ramp Loss SVM with L1-Norm Regularization" by Eric J. Hess and J. Paul Brooks, presented at the 2015 INFORMS Computing Society Conference, Operations Research and Computing: Algorithms and Software for Analytics, Richmond, Virginia January 11-13, 2015.

The files contain instances of optimization problems that are described in the paper and for which results are reported. The files are in CPLEX LP format. The naming convention of the files is as follows: ndBTj0F.lp, where is the number of samples, is the number of attributes, and refers to …