Datasets:
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अक |
आनिॽ |
आप |
आबाङे |
आल |
इक |
इङ |
क |
कन |
कर |
काक |
काङ |
किन |
किल |
कुइसिःक |
कुसिङ |
के |
केःल |
केजोःक |
केरेक |
केलॽबा |
केलॽरिक |
को |
खरिस |
खाम |
खिनिॽ |
खुनिॽ |
खुनेॽ |
खे |
खेङ |
खेन |
खेप |
खेल |
खेलॽरिक |
ग |
गाॽ |
गेन |
ङाङ |
ङिन |
ङिल |
ङो |
चोःक |
चोगुल |
छाम |
छिङ |
जुम |
त |
तङ |
ताङ |
तुक |
तुम |
तेन |
थक |
थारिक |
थेआङ |
दाङ |
दिङ |
नसाःन |
नाङ |
निङ |
निन |
नु |
नुःबा |
ने |
नो |
पत |
पा |
पाःन |
पाङ |
पोःक |
पोःङ |
फाॽआङ |
फाॽगर |
फेन |
बक |
बा |
बाल |
भेल |
मन |
मनाबा |
मनाहाॽ |
मनाहाॽरे |
मा |
माङ |
माॽ |
मि |
मिन |
मिल |
मेःन |
मेन |
मेॽओ |
मो |
यम |
यरिक |
याःम |
याङ |
याप |
याम |
युक |
येम |
target-stopwords
Per-language function-word lists, induced from that language's own Bible text — frequency +
dispersion (the classic corpus-linguistics stopword-induction recipe), then RESCUED against the
language's own alignment output + a source-anchored content signal so genuinely frequent CONTENT words
("God", "Lord") are never dropped. See the lexeme-aligner's target_stopwords.py for the mechanism: a
candidate is rescued if it concentrates most of its aligned mass on one prior-pack content lexeme (≥40%
share) — a true function word instead scatters thinly across dozens of distinct lexemes.
Many of the covered languages have no existing curated stopword list anywhere — this is a reusable resource for search, IR, topic modeling, or any NLP task needing one in these languages.
CC0-1.0 — derived word-frequency statistics, no source text redistributed. See manifest.json for
per-language stats + content hashes.
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