[Submitted on 23 Jun 2026]
Title:Text Distance from Nested and Hierarchical Repetitions: A Compression-Based Perspective
View a PDF of the paper titled Text Distance from Nested and Hierarchical Repetitions: A Compression-Based Perspective, by Xiaojun Hu and 7 other authors
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Abstract:We present a new method for structural sequence analysis grounded in Algorithmic Information Theory (AIT). At its core is the Ladderpath approach, which extracts nested and hierarchical relationships among repeated substructures in linguistic sequences -- an instantiation of AIT's principle of describing data through minimal generative programs. These structures are then used to define three distance measures: a normalized compression distance (NCD), and two alternative distances derived directly from the Ladderpath representation. Integrated with a $k$-nearest neighbor classifier, these distances achieve strong and consistent performance across in-distribution, out-of-distribution (OOD), and few-shot text classification tasks. In particular, all three methods outperform both gzip-based NCD and BERT under OOD and low-resource settings. These results demonstrate that the structured representations captured by Ladderpath preserve intrinsic properties of sequences and provide a lightweight, interpretable, and training-free alternative for text modeling. This work highlights the potential of AIT-based approaches for structural and domain-agnostic sequence understanding.
Subjects:
Computation and Language (cs.CL); Information Theory (cs.IT)
Cite as: arXiv:2607.05416 [cs.CL]
(or arXiv:2607.05416v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.05416
arXiv-issued DOI via DataCite
Submission history
From: Yu Liu Prof. [view email] [v1] Tue, 23 Jun 2026 03:20:57 UTC (2,155 KB)
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