bcv-commons/aligned-mwe
aligned_mwe — multi-word target expressions per lexeme Where lexeme-alignments is one row per surface token, this is one row per lexeme rendered by a contiguous multi-word phrase (חֶסֶד → "kasih setia", בֵּית → "tempat pengirikan"). Mined from the aligner's per-verse t_idx positions: only spans whose target token positions are contiguous (max−min+1 == len) qualify — scattered tokens that merely all linked to a lexeme are dropped (and counted in the manifest as scattered_dropped… See the full description on the dataset page: https://huggingface.co/datasets/bcv-commons/aligned-mwe.
aligned_mwe — multi-word target expressions per lexeme
Where `lexeme-alignments` is one row per surface token, this is one row per lexeme rendered by a contiguous multi-word phrase (חֶסֶד → "kasih setia", בֵּית → "tempat pengirikan"). Mined from the aligner's per-verse t_idx positions: only spans whose target token positions are contiguous (max−min+1 == len) qualify — scattered tokens that merely all linked to a lexeme are dropped (and counted in the manifest as scattered_dropped, never silently). Rides on eflomal's grow-diag-final-and symmetrised alignment.
Schema (per row)
Licensing — CC0-1.0
Phrases + counts + public identifiers (Strong's / MACULA lexeme), no MACULA analytical columns — same basis as lexeme-alignments. Each language's source translation keeps its own terms; see the per-language source pointer in manifest.json.
Regenerate: python -m lexeme_aligner.export_mwe --iso <iso> --method merged. Bulk Parquet is git-ignored and published out-of-band; only manifest.json (per-language metadata + content_sha256) and this card are committed.