[2512.22196] AETAS: Analysis of Evolving Temporal Affect and Semantics for Legal History
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[2512.22196] AETAS: Analysis of Evolving Temporal Affect and Semantics for Legal History

arXiv - Machine Learning 3 min read

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Abstract page for arXiv paper 2512.22196: AETAS: Analysis of Evolving Temporal Affect and Semantics for Legal History

Computer Science > Digital Libraries arXiv:2512.22196 (cs) [Submitted on 20 Dec 2025 (v1), last revised 21 Mar 2026 (this version, v2)] Title:AETAS: Analysis of Evolving Temporal Affect and Semantics for Legal History Authors:Qizhi Wang View a PDF of the paper titled AETAS: Analysis of Evolving Temporal Affect and Semantics for Legal History, by Qizhi Wang View PDF HTML (experimental) Abstract:Digital-humanities work on semantic shift often alternates between handcrafted close readings and opaque embedding machinery. We present a reproducible expert-system style pipeline that quantifies lexical drift and its instability in the Old Bailey Corpus (1674-1913), coupling interpretable trajectories with legally meaningful axes. We bin proceedings by decade with dynamic merging for low-resource slices, train skip-gram embeddings, align spaces through orthogonal Procrustes to a 1900s anchor, and measure both geometric displacement and neighborhood turnover. We add split-half baselines and seed-sensitivity checks to separate within-bin instability from temporal change. Three visual analytics outputs (drift magnitudes, semantic trajectories, and movement along a mercy-versus-retribution axis) expose how justice, crime, poverty, and insanity evolve with penal reforms, transportation debates, and Victorian moral politics. The pipeline is implemented as auditable scripts so results can be reproduced in other historical corpora. Comments: Subjects: Digital Libraries (cs.DL); Computers a...

Originally published on March 24, 2026. Curated by AI News.

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