基于递归量化分析的动态自然语言处理
Dynamic Natural Language Processing with Recurrence Quantification Analysis
摘要：
阅读和写作是一个动态的过程。当一个作者创作一个文本时，一系列的单词就产生了。作者希望，这一序列能够引起对他人某些思想和观念的重新审视。这些写作和读者再访的过程是按时间顺序安排的。这意味着文本本身可以在动力系统的镜头下进行研究。递归量化分析（Recurrence Quantitative Analysis，RQA）是一种分析动态系统行为的常用方法，可以用来分析文本的序列结构。RQA将文本视为一个连续的测量，就像一个时间序列，因此可以被视为一种动态的自然语言处理（NLP）。扩展有几个好处。由于它是一套时间序列分析工具的一部分，因此可以在一个公共框架中提取许多度量。其次，这些度量与自然语言处理中一些常用的度量有着密切的关系。最后，使用递归分析提供了一个机会，通过开发来自复杂动态系统的理论描述来扩展文本分析。我们展示了对古腾堡项目中8,000个文本的示例分析，将其与众所周知的NLP方法进行了比较，并描述了可以与R库crqa结合使用的R包（crqanlp）。
英文摘要：
Writing and reading are dynamic processes. As an author composes a text, a sequence of words is produced. This sequence is one that, the author hopes, causes a revisitation of certain thoughts and ideas in others. These processes of composition and revisitation by readers are ordered in time. This means that text itself can be investigated under the lens of dynamical systems. A common technique for analyzing the behavior of dynamical systems, known as recurrence quantification analysis (RQA), can be used as a method for analyzing sequential structure of text. RQA treats text as a sequential measurement, much like a time series, and can thus be seen as a kind of dynamic natural language processing (NLP). The extension has several benefits. Because it is part of a suite of time series analysis tools, many measures can be extracted in one common framework. Secondly, the measures have a close relationship with some commonly used measures from natural language processing. Finally, using recurrence analysis offers an opportunity expand analysis of text by developing theoretical descriptions derived from complex dynamic systems. We showcase an example analysis on 8,000 texts from the Gutenberg Project, compare it to well-known NLP approaches, and describe an R package (crqanlp) that can be used in conjunction with R library crqa.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
