research-metadataannotation

Hypothesis: Document structure complexity predicts the value added by metadata-augmented annotation for retrieval tasks.

Overview

This package benchmarks whether adding structural metadata (section count, table presence, figure presence) to document annotations yields measurable recall gains — and whether those gains correlate with document complexity.

Experiment Table

Domain	Baseline Recall	Metadata Recall	Mean Gain	Mean Complexity
Finance	0.55	0.71	0.16	0.52
Legal	0.58	0.77	0.19	0.61
Medical	0.52	0.69	0.17	0.55
Technical	0.60	0.80	0.20	0.67

Domain Breakdown

• Finance: Moderately complex documents; earnings reports benefit from table metadata.
• Legal: High section count drives complexity; clause-level metadata yields largest gains.
• Medical: Mixed figures/tables; imaging reports gain most from figure metadata.
• Technical: Highest complexity; API documentation and specs show strongest metadata value.

Methodology

1. Compute StructureComplexity.complexity_score for each document.
2. Annotate with and without metadata (baseline vs. metadata-augmented pipeline).
3. Fit StructurePredictor to learn the linear relationship between complexity and recall gain.
4. Evaluate via Pearson r and R².

Venue

Submitted to ENCODE 2026 — Workshop on Encoding Knowledge in Neural Systems.

Citation

@inproceedings{anote2026metadata,
  title     = {Structure Complexity Predicts Metadata Annotation Value in Document Retrieval},
  author    = {Anote AI},
  booktitle = {ENCODE 2026},
  year      = {2026},
}