Uncovering the Complexity of Text Semantics through Multimodal Deep Learning Models

In this study, we propose a novel methodology to assess the conceptual diversity of texts, which measures the richness and complexity of a text’s content from a reader’s perspective. Our approach is inspired by entropy and follows a step-by-step process to calculate the concept frequency in a pool of noun concepts, assigning probabilities using probability equations. We demonstrate the effectiveness and reliability of our proposed "concept diversity metric" through experiments on various texts, showing that it provides a comprehensive understanding of the richness and complexity of textual content.
Our methodology is applicable because its complexity is O(logN+2N), which is reasonable, and it takes into account both literal and hidden concepts inside all possible concepts. We also show that our scores are parallel with human views, as the density of concepts, detail, and generality are considered.
In recent years, natural language processing (NLP) has made significant progress, and generative AI has enabled chatbots to communicate with humans. However, evaluating and comparing text outputs remain challenging due to the complexity of training algorithms with billions of parameters. While there are many evaluation metrics available, they are not enough, and human evaluation is still necessary.
To address these issues, we propose a new metric to assess a text’s generality or level of information from a conceptual perspective, scaling the number of concepts across all of them. We also demonstrate that our proposed metric is independent of the size of the text.
The article discusses the limitations of existing evaluation metrics and the importance of considering the conceptual diversity of texts. Our proposed metric provides a comprehensive understanding of the richness and complexity of textual content, which is essential for improving NLP applications. By providing a new evaluation metric that considers the conceptual diversity of texts, we hope to contribute to the development of more accurate and effective NLP systems in the future.