Datasets:
text stringlengths 0 21 |
|---|
a |
abisa |
afa |
ah |
ai |
am |
ama |
an |
ana |
anababatun |
anayabin |
anun |
ao |
at |
ata |
atube |
au |
auman |
ayu |
babin |
bai |
bairi |
baise |
baitumatum |
boro |
boun |
bowabow |
ef |
ema |
en |
eo |
etei |
fair |
gagamin |
gewas |
gewasin |
god |
hai |
hairi |
hi |
hina |
hio |
i |
imaim |
imih |
imon |
in |
isa |
isah |
isan |
it |
iti |
itin |
itinin |
itube |
iyab |
jesu |
kakafih |
kakafin |
kakafiyin |
keriso |
kok |
kwa |
kwana |
kwanekwan |
ma |
maiye |
maiyow |
mar |
men |
merar |
mi |
na |
naatu |
nama |
nati |
o |
ob |
orot |
regah |
sabuw |
saise |
sawar |
sinaf |
ta |
tafaram |
tur |
turobe |
tutufin |
ub |
veya |
wan |
wanatowan |
wanawanan |
wanawananamaim |
yait |
yawas |
aga |
agwɨ |
aiwɨ |
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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