DASTEX: a New Readability Formula based on Semantic Complexity of Text
Simple measures often couldn’t count a deep complexity. In the case of semantic complexity of the text, conventional readability formulas share a common style, a common sort of achievements and a common borders of limitation: These formulas lack a semantics-aware approach and as a result, a precise measurement of semantic complexity couldn’t be done by them. In this paper, we introduce DASTEX, a novel semantics-aware complexity measure for semantic complexity of text. By DASTEX, a new layer of complexity analysis are opened for NLP, cognitive and computational tasks. This measure benefits from an intuitionistic underlying formal model which consider semantic as a lattice of intuitions. This yields to a well-defined definition for semantic of a text and its complexity. DASTEX is a practical analysis method upon this formal model. So a complete suite of idea, model and method are prepared to result in a simple but yet deep measure for semantic complexity of text. The evaluation of the proposed approach is done by a detailed example, a case study, a set of eighteen human-judgment experiments and a corpus-based evaluation. The results show that DASTEX is capable of measuring the semantic complexity of text. The Experiment-results demonstrate that our method consistently outperforms the random baseline in terms of better precision and accuracy.