Stop Removing Stopwords: How an Inherited Preprocessing Default Distorts Legal Text-as-Data
Empirical legal scholarship increasingly treats judicial text as data, and much of it still runs on sparse, interpretable pipelines (TF-IDF features and linear classifiers) because the textual feature is often the object of study rather than a means to a prediction. Yet these pipelines inherit preprocessing defaults from mid-century information retrieval that were never validated against classification accuracy. The most entrenched of these is stopword removal. This study introduces an exhaustive single-word ablation that measures a preprocessing step's effect directly against the downstream objective, and applies it to stopword removal. Matching Supreme Court Database labels to Caselaw A...
| Authors | Gregory M. Dickinson |
|---|---|
| Primary category | cs.CL |
| Published | 21 July 2026 |
| Last updated at source | 4 October 2026 |
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