mlsanityData to 7 Oct 2026

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

arXiv 2512.22196 ↗First posted 20 Dec 2025cs.DL cs.CY cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 20 Dec 2025.

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References · 8

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
  3. 2020SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detectioncited by 105
  4. 2020Analysing Lexical Semantic Change with Contextualised Word Representationscited by 92
  5. 2018Diachronic word embeddings and semantic shifts: a surveycited by 90
  6. 2018Survey of Computational Approaches to Lexical Semantic Changecited by 60
  7. 2017Dynamic Word Embeddingscited by 51
  8. 2017Incremental Skip-gram Model with Negative Samplingcited by 4

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