Text Distance from Nested and Hierarchical Repetitions: A Compression-Based Perspective
Quick Answer
The Ladderpath approach introduces a novel method for analyzing nested and hierarchical relationships in linguistic sequences, leveraging Algorithmic Information Theory.
Quick Take
This method provides three distance measures that outperform gzip-based NCD and BERT in out-of-distribution and low-resource text classification tasks, highlighting its potential for lightweight and interpretable text modeling.
Key Points
- Ladderpath extracts nested relationships in linguistic sequences using Algorithmic Information Theory.
- Three distance measures include normalized compression distance and two derived from Ladderpath.
- Achieves superior performance in OOD and few-shot text classification compared to gzip-based NCD and BERT.
- Demonstrates a lightweight, interpretable, and training-free alternative for text modeling.
- Highlights the potential of AIT-based methods for structural sequence understanding.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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