In this paper we build on the abstractions made available in Saul programming language (Kordjamshidi et al., 2015), in order to create a unified and flexible machine learning programming framework using the generic Constrained-Conditional-Model (CCM) paradigm (Chang et al., 2012).
we build upon Saul's graph-based data representation and enrich it with primitive structures and sensors for the NLP domain that facilitate operating at arbitrary levels of 'globality' for learning and inference.