  The use of natural language processing (NLP) is gaining popularity in
software engineering. In order to correctly perform NLP, we must pre-process
the textual information to separate natural language from other information,
such as log messages, that are often part of the communication in software
engineering. We present a simple approach for classifying whether some textual
input is natural language or not. Although our NLoN package relies on only 11
language features and character tri-grams, we are able to achieve an area under
the ROC curve performances between 0.976-0.987 on three different data sources,
with Lasso regression from Glmnet as our learner and two human raters for
providing ground truth. Cross-source prediction performance is lower and has
more fluctuation with top ROC performances from 0.913 to 0.980. Compared with
prior work, our approach offers similar performance but is considerably more
lightweight, making it easier to apply in software engineering text mining
pipelines. Our source code and data are provided as an R-package for further
improvements.
