  It is presented here a machine learning-based (ML) natural language
processing (NLP) approach capable to automatically recognize and extract
categorical and numerical parameters from a corpus of articles. The approach
(named a.RIX) operates with a concomitant/interchangeable use of ML models such
as neuron networks (NNs), latent semantic analysis (LSA), naive-Bayes
classifiers (NBC), and a pattern recognition model using regular expression
(REGEX). A corpus of 7,873 scientific articles dealing with natural products
(NPs) was used to demonstrate the efficiency of the a.RIX engine. The engine
automatically extracts categorical and numerical parameters such as (i) the
plant species from which active molecules are extracted, (ii) the
microorganisms species for which active molecules can act against, and (iii)
the values of minimum inhibitory concentration (MIC) against these
microorganisms. The parameters are extracted without part-of-speech tagging
(POS) and named entity recognition (NER) approaches (i.e. without the need of
text annotation), and the models training is performed with unsupervised
approaches. In this way, a.RIX can be essentially used on articles from any
scientific field. Finally, it can potentially make obsolete the current article
reviewing process in some areas, especially those in which machine learning
models capture texts structure, text semantics, and latent knowledge.
