 
Several organizations need to analyse
texts and documents automatically,
in order to reduce working time
and improve the quality of their decisions.
The ongoing public project
aims at ensuring the correspondence between actions planned
by companies  to contain their employees’ risks of injury,
and the public funds to be allocated for the same purpose.
Texts written in natural language
are particularly hard to analyse.
It is necessary to understand their semantic content and
identify any existing association with other reference data.
Several text mining techniques can be used to detect
explicit or hidden relations.
They range from measuring text distance or similarity,
to text classification, or again to identifying clusters,
patterns, maps and semantic structures.
 
The system processes thousands of requests every year
and it analyses the existing relation between the content of the application form and the
companies’ attributes, the risks to be contained,
and the effectiveness of past similar actions.
Thanks to Machine Learning techniques,
the explorative analysis allows you to highlight specific
characteristics and perspectives.
The resulting information can be compared
with effectiveness measures
in order to decide whether funds shall be granted or not.
This activity has demonstrated the suitability of the automation process,
which has allowed the organization to select
the application forms with better chance of succeeding,
enabling it to allocate funds in a more focused way
reducing resource waste.
