DAST Model: Deciding About Semantic Complexity of a Text
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Measuring of text complexity is a needed task in several domains and applications (such as NLP, semantic web, smart education and etc.). The Semantic layer of a text is more tacit than its syntactic structure and as a result, calculation of semantic complexity is more difficult. Whereas there are famous and powerful academic and commercial syntactic complexity measures, the problem of measuring Semantic complexity is a challenging one, yet. In this article, we introduce the DAST model which stands for Deciding About Semantic Complexity of a Text. In this model, an intuitionistic approach to semantics lets us have a well-defined definition for semantic of a text and its complexity: we consider semantic and meaning as a lattice of intuitions. 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 detailed example and a case study, a set of eighteen human-judgment experiments and a corpus-based evaluation. The results show that DAST model is capable of deciding about semantic complexity of a text. Furthermore, Analysis of the experiment results leads us to introduce a Markovian model for the process of common-sense multi-steps semantic-complexity reasoning in people. The Experiments-result demonstrates that our method consistently outperforms the random baseline in terms of better precision and accuracy.
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