semantic-neighbors / README.md
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---
license: cc0-1.0
pretty_name: "Hebrew Semantic Neighbor Pairs — cross-signal + LLM verified"
language:
- hbo
task_categories:
- text-classification
tags:
- biblical-hebrew
- semantic-similarity
- lexicography
configs:
- config_name: default
data_files: "published_pairs.tsv"
---
# Hebrew Semantic Neighbor Pairs
7,977 Strong's-number pairs of Biblical Hebrew words judged to be synonyms or same tight semantic
concept, at a publication-confidence level reached through three independent verification methods
rather than any single signal. CC0 — no restricted sources.
## Method
This dataset is the *confident subset* of a larger semantic-neighbor pipeline that combines ten
independent signals: distributional embeddings (BEREL), LXX cross-testament co-rendering, gloss
overlap, an LLM scholarly-prior pass, Brown-Driver-Briggs (1906, public domain) etymological roots,
T'OMIM poetic-parallelism pairs, Hebrew WordNet, BHSA syntactic structure (coordination/apposition via
Context-Fabric), a cross-lingual/Wiktionary-root corroboration tier, and candidates from Radak's Sefer
HaShorashim (a 13th-century Public Domain medieval Hebrew root dictionary, via Sefaria). Individually,
these signals range from weak (~35%) to strong (~90%).
Rather than trust any single signal's raw output, a pair is published here only if it passes one of
three independent confidence gates:
- **`cross_signal`** — asserted by >= 2 methodologically independent signal families (see the
`gate` column). 70.2% SDBH `core`-agreement on its own.
- **`llm_verified`** — pairs that only ever had ONE signal behind them, individually judged by an LLM
(Claude Haiku, strict "are these genuinely synonyms in Biblical Hebrew usage" prompt). Only "yes"
verdicts included. 69.5% on its own.
- **`sefer_hashorashim_verified`** — candidates from Sefer HaShorashim (words Radak discusses together
within one root entry), also LLM-verified the same way. 62.9% on its own (its unique contribution
after removing overlap with the other two gates).
1,241 pairs pass more than one gate — that subset scores 78.4%, the highest-confidence layer in the
dataset (see the `gate` column, "+"-joined for multi-gate pairs).
## Columns
| column | meaning |
|---|---|
| `strong_a` / `strong_b` | Hebrew Strong's numbers (`H####`) |
| `gate` | which confidence check(s) this pair passed — `cross_signal`, `llm_verified`, `sefer_hashorashim_verified`, or a "+"-joined combination |
| `n_families` | how many independent signal families asserted this pair (0 if only LLM-verified) |
| `llm_verdict` | `yes` if LLM-verified (either LLM gate); blank if published via cross-signal agreement alone |
## Validation caveat — read before treating the percentages as ground truth
Every accuracy number associated with this pipeline is measured as **agreement with SDBH's own domain
codes** (the Semantic Dictionary of Biblical Hebrew, © United Bible Societies) as an internal proxy —
not an independent audit, and not a claim of "correctness." Two things follow:
1. SDBH is a scholarly taxonomy, not exhaustive ground truth — a real synonym pair can fail to share an
SDBH domain code simply because SDBH's own domain boundaries split it, and (separately) SDBH data
itself is licensed "used with permission" (not CC-BY) and is **not included or derivable from this
dataset** — nothing here reproduces SDBH's domain codes or text.
2. These percentages describe how well this dataset agrees with one particular scholarly resource, not
an absolute correctness rate. Treat them as methodology transparency, not a quality guarantee.
## License
CC0-1.0. Built from: BDB (1906, public domain), T'OMIM (CC BY 4.0), Hebrew WordNet (Ordan & Wintner,
permissive), Wiktionary (CC BY-SA), BHSA/Context-Fabric (public-domain WLC + CC BY 4.0 ETCBC
annotation), Sefer HaShorashim (Radak, c.1185-1235 CE, Public Domain, via Sefaria), and an LLM
verification pass (Claude Haiku) — none of these require restricted-use attribution beyond what's
already satisfied by citing this dataset.
## Provenance
Built by `shoresh/macula/build_published_pairs.py` in
[bcv-commons/bcv-query](https://github.com/bcv-commons/bcv-query).