  Measuring text complexity is an essential task in several fields and
applications (such as NLP, semantic web, smart education, etc.). The semantic
layer of text is more tacit than its syntactic structure and, as a result,
calculation of semantic complexity is more difficult than syntactic complexity.
While there are famous and powerful academic and commercial syntactic
complexity measures, the problem of measuring semantic complexity is still a
challenging one. In this paper, we introduce the DAST model, which stands for
Deciding About Semantic Complexity of a Text. DAST proposes an intuitionistic
approach to semantics that lets us have a well-defined model for the semantics
of a text and its complexity: semantic is considered as a lattice of intuitions
and, as a result, semantic complexity is defined as the result of a calculation
on this lattice. A set theoretic formal definition of semantic complexity, as a
6-tuple formal system, is provided. By using this formal system, a method for
measuring semantic complexity is presented. The evaluation of the proposed
approach is done by a set of three human-judgment experiments. The results show
that DAST model is capable of deciding about semantic complexity of text.
Furthermore, the analysis of the results leads us to introduce a Markovian
model for the process of common-sense, multiple-steps and semantic-complexity
reasoning in people. The results of Experiments demonstrate that our method
outperforms the random baseline with improvement in better precision and
competes with other methods by less error percentage.
