A Partial Differential Equation Models Based Framework for Text Analysis and Interpretability in English Reading Corpora

A Partial Differential Equation Models Based Framework for Text Analysis and Interpretability in English Reading Corpora | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 March 2025 V1 Latest version Share on A Partial Differential Equation Models Based Framework for Text Analysis and Interpretability in English Reading Corpora Authors : Li Xiaolong [email protected] , Zhang Haiyan , and Li Xiaolong Authors Info & Affiliations https://doi.org/10.22541/au.174098547.76269809/v1 254 views 89 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Partial Differential Equation (PDE) models have emerged as a powerful tool in various domains due to their ability to capture dynamic relationships among complex variables. In the field of linguistics, particularly in English reading corpora, the technology has shown immense potential for the detection of semantic and syntactic patterns, enabling significant advancements in text analysis and natural language processing. However, existing methods for text analysis often fail to adequately model the intricate dependencies and multi-dimensional relationships inherent in large datasets. To address these challenges, this paper proposes a novel PDE-based framework tailored for English reading corpora. Current methods, such as neural networks and statistical models, are limited by their reliance on large datasets, lack of explainability, and inability to integrate structural and temporal linguistic features seamlessly. The proposed scheme uses the strengths of PDEs to overcome these limitations by introducing a mathematically rigorous and interpretable approach to text modeling. This paper adopts a methodology rooted in PDE formulations. Firstly, linguistic features, including lexical, syntactic, and semantic attributes, are extracted and mathematically represented. Secondly, a set of PDEs is designed to model these features’ temporal and spatial dynamics across the text corpus. Thirdly, numerical methods such as finite difference and finite element techniques are employed to solve the PDEs, yielding insights into the structural and semantic evolution of the corpus. Finally, a comparative evaluation is performed to assess the model’s performance against traditional and neural network-based approaches. The experimental results show the efficacy of the proposed framework, with significant improvements observed in accuracy (94.1%), precision (90.2%), recall (92.1%), F1 score (91.1%), and AUC (0.945), alongside competitive execution times. Compared with state-of-the-art methods, such as CNNs, LSTMs, and SVMs, the proposed PDE model consistently outperforms predictive accuracy and interpretability, showcasing its potential as a transformative approach for text analysis in English reading corpora. Supplementary Material File (manuscript.docx) Download 1.94 MB Information & Authors Information Version history V1 Version 1 03 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords computational linguistics english reading corpora interpretability machine learning partial differential equations (pde) semantic analysis syntactic analysis Authors Affiliations Li Xiaolong [email protected] Anhui University School of Foreign Studies View all articles by this author Zhang Haiyan Anhui University School of Foreign Studies View all articles by this author Li Xiaolong Hefei University View all articles by this author Metrics & Citations Metrics Article Usage 254 views 89 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Li Xiaolong, Zhang Haiyan, Li Xiaolong. A Partial Differential Equation Models Based Framework for Text Analysis and Interpretability in English Reading Corpora. Authorea . 03 March 2025. DOI: https://doi.org/10.22541/au.174098547.76269809/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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