{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dynamic-natural-language-processing-with","title":"Dynamic Natural Language Processing with Recurrence Quantification Analysis","arxiv_id":"1803.07136","date":"2018-03-19","proceeding":null,"authors":["Rick Dale","Nicholas D. Duran","Moreno Coco"],"abstract":"Writing and reading are dynamic processes. As an author composes a text, a\nsequence of words is produced. This sequence is one that, the author hopes,\ncauses a revisitation of certain thoughts and ideas in others. These processes\nof composition and revisitation by readers are ordered in time. This means that\ntext itself can be investigated under the lens of dynamical systems. A common\ntechnique for analyzing the behavior of dynamical systems, known as recurrence\nquantification analysis (RQA), can be used as a method for analyzing sequential\nstructure of text. RQA treats text as a sequential measurement, much like a\ntime series, and can thus be seen as a kind of dynamic natural language\nprocessing (NLP). The extension has several benefits. Because it is part of a\nsuite of time series analysis tools, many measures can be extracted in one\ncommon framework. Secondly, the measures have a close relationship with some\ncommonly used measures from natural language processing. Finally, using\nrecurrence analysis offers an opportunity expand analysis of text by developing\ntheoretical descriptions derived from complex dynamic systems. We showcase an\nexample analysis on 8,000 texts from the Gutenberg Project, compare it to\nwell-known NLP approaches, and describe an R package (crqanlp) that can be used\nin conjunction with R library crqa.","url_abs":"http://arxiv.org/abs/1803.07136v1","url_pdf":"http://arxiv.org/pdf/1803.07136v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dynamic-natural-language-processing-with","repo_url":"https://github.com/racdale/crqanlp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}