Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentation

• URL: http://arxiv.org/abs/2510.03437v1
• Date: Fri, 03 Oct 2025 18:57:22 GMT
• Title: Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentation
• Authors: Jairo Diaz-Rodriguez, Mumin Jia,
• Abstract summary: Kernel change-point detection (KCPD) has become a widely used tool for identifying structural changes in complex data.<n>We prove consistency in the number of detected change points and weak consistency in locations under mild additional assumptions.<n>We present the first empirical study of KCPD for text segmentation with modern embeddings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Kernel change-point detection (KCPD) has become a widely used tool for identifying structural changes in complex data. While existing theory establishes consistency under independence assumptions, real-world sequential data such as text exhibits strong dependencies. We establish new guarantees for KCPD under $m$-dependent data: specifically, we prove consistency in the number of detected change points and weak consistency in their locations under mild additional assumptions. We perform an LLM-based simulation that generates synthetic $m$-dependent text to validate the asymptotics. To complement these results, we present the first comprehensive empirical study of KCPD for text segmentation with modern embeddings. Across diverse text datasets, KCPD with text embeddings outperforms baselines in standard text segmentation metrics. We demonstrate through a case study on Taylor Swift's tweets that KCPD not only provides strong theoretical and simulated reliability but also practical effectiveness for text segmentation tasks.

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