Koponen Ismo, Södervik Ilona
Department of Physics, University of Helsinki, 00014 Helsinki, Finland.
Centre for University Teaching and Learning (HYPE), University of Helsinki, 00014 Helsinki, Finland.
Entropy (Basel). 2022 Jul 31;24(8):1058. doi: 10.3390/e24081058.
Complex networks are often used to analyze written text and reports by rendering texts in the form of a semantic network, forming a lexicon of words or key terms. Many existing methods to construct lexicons are based on counting word co-occurrences, having the advantage of simplicity and ease of applicability. Here, we use a quantum semantics approach to generalize such methods, allowing us to model the entanglement of terms and words. We show how quantum semantics can be applied to reveal disciplinary differences in the use of key terms by analyzing 12 scholarly texts that represent the different positions of various disciplinary schools (of conceptual change research) on the same topic (conceptual change). In addition, attention is paid to how closely the lexicons corresponding to different positions can be brought into agreement by suitable tuning of the entanglement factors. In comparing the lexicons, we invoke complex network-based analysis based on exponential matrix transformation and use information theoretic relative entropy (Jensen-Shannon divergence) as the operationalization of differences between lexicons. The results suggest that quantum semantics is a viable way to model the disciplinary differences of lexicons and how they can be tuned for a better agreement.
复杂网络通常用于通过将文本呈现为语义网络的形式来分析书面文本和报告，从而形成单词或关键术语的词汇表。许多现有的构建词汇表的方法基于计算单词共现，具有简单易用的优点。在这里，我们使用量子语义方法来推广此类方法，使我们能够对术语和单词的纠缠进行建模。我们展示了如何通过分析12篇学术文本（代表了不同学科流派（概念变化研究）在同一主题（概念变化）上的不同立场）来应用量子语义揭示关键术语使用中的学科差异。此外，还关注如何通过对纠缠因子进行适当调整，使对应于不同立场的词汇表达成更紧密的一致。在比较词汇表时，我们基于指数矩阵变换调用基于复杂网络的分析，并使用信息理论相对熵（詹森-香农散度）作为词汇表之间差异的操作化度量。结果表明，量子语义是一种可行的方法，可用于对词汇表的学科差异进行建模，以及如何对其进行调整以达成更好的一致。
