<![CDATA[Transcribing natural language to visualization (NL2VIS) has been investigated for years but still suffer from several fundamental limitations (e.g., feature selection). Although large language models (LLMs) are good candidates but they incur computation cost and hard to trace their made decisions. To alleviate this problem, we introduced an alternative information-theoretic framework that utilized mutual information (MI) to quantify the statistical relationship between utterances and database features. In our approach, kernel density estimation (KDE) and neural estimation techniques were utilized to estimate MI, and to optimize a diversity-promoting objective balancing feature relevance and redundancy. We also introduced the information coverage ratio (ICR) to quantify the amount of information content preserved in feature selection decisions. In our experiments, we found that the proposed approach improved information-theoretic metrics, with F1-score of 0.863 and an ICR of 0.891. We observed that these improvements did not come at the cost of traditional benchmarks: validity reached 88.9%, legality 85.2%, and chart-type accuracy 87.6%. Moreover, significance tests (p < 0.001) and large effect sizes (Cohen’s d > 0.8) further supported that these improvements were meaningful for feature selection. Thus, this study provides a mathematical framework for applications requiring analytical validity that extends beyond NL2VIS to other machine learning contexts.]]>
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