An experimentation line for underlying graphemic properties acquiring knowledge from text data with Self Organizing Maps

We present an experimentation line that encompasses various stages for research on graphemes distribution and unsupervised classification. We aim to help close the gap between recent research results showing the abilities of unsupervised learning and clustering algorithms to detect underlying properties of phonemes and the present possibilities of Unicode textual representation. Our procedures need to ensure repeatability and guarantee that no information is implicitely present in the preprocessing of data. Our approach is able to categorize potential graphemes correctly, thus showing that not only phonemic properties are indeed present in textual data, but that they can be automatically retrieved from raw-unicode text data and translated into phonemic representations. By the way, we observe that SOM algorithm copes well with very sparse vectors.