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Guillaume Segerer CNRS - LLACAN - France segerer@vjfrs.fr

Guillaume Segerer CNRS - LLACAN - France segerer@vjf.cnrs.fr. Niger-Congo Languages as a playground for lexical comparison. LYON, May 12-14, 2008 New Directions in Historical Linguistics Paper presented May 14 Document revised May 16. Languages of Africa. Niger-Congo Languages.

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Guillaume Segerer CNRS - LLACAN - France segerer@vjfrs.fr

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  1. Guillaume SegererCNRS - LLACAN - Francesegerer@vjf.cnrs.fr Niger-Congo Languages as a playground for lexical comparison LYON, May 12-14, 2008 New Directions in Historical Linguistics Paper presented May 14 Document revised May 16

  2. Languages of Africa

  3. Niger-Congo Languages

  4. Niger-Congo clusters

  5. The experiment The present experiment consists in: - testing the validity of the Niger-Congo phylum by measuring its homogeneity - doing real mass comparison : 506 languages examined (~ 1/3 of all NC languages) - with only a few lexical roots, chosen intuitively from empirical experience It can be further refined by: - considering more languages (more data is actually available) - chosing different lexical roots (but how ?) - taking into account adjacent phyla (Nilo-Saharan, Afro-Asiatic, Khoisan)

  6. Language Sample (506 lgs)

  7. 10 supposedly common NC lexical roots 1 TU(P) : to spit 2 MED ~ MOD : to swallow 3 NYU : to drink 4 DUM : to bite 5 TE : tree 6 NYI(N) : tooth 7 TU : ear 8 DEM : tongue 9 DI : to eat 10 TAT : three

  8. Distribution of root 1

  9. Distribution of root 2

  10. Distribution of root 3

  11. Distribution of root 4

  12. Distribution of root 5

  13. Distribution of root 6

  14. Distribution of root 7

  15. Distribution of root 8

  16. Distribution of root 9

  17. Distribution of root 10

  18. Weighted sample

  19. Probabilities 1 consonants labial : symbol P dental/coronal : symbol T palatal : symbol C velar/uvular : symbol K vowels front : symbol I central : symbol A back : symbol U example DEM ‘tongue’ coded as TIP probability 1/4 x 1/3 x 1/4 = 1/48 toleranceI~A > new prob. 1/24 Out of the 506 sample languages, 1/24 = 21 languages may by chance have a word for ‘tongue’ of the shape TIP ~ TAP

  20. Probabilities 2 Probabilities for each of the 10 roots TAT - three : TAT ~ TIT > 1/24 DUM - bite : TUP ~ TUT > 1/24 DEM - tongue : TIP ~ TAP > 1/24 MED ~ MOD - swallow : PIT ~ PUT > 1/24 TU(P) - spit : TU ~ CU > 1/6 TU - ear : TU ~ CU > 1/6 NYI(N) - tooth : CI ~ TI > 1/6 TE - tree : TI ~ TA ~ CI > 1/4 DI - eat : TI ~ CA ~ CI > 1/4 NYU - drink : CU ~ TU ~ KU> 1/3 probability to have all 10 items : 1/ 143 327 232 4 languages in the sample have all 10 items : Akpafu (Kwa), Sukuma (Bantu F21, Runyankore (Bantu E13), Andoni (Benue-Congo)

  21. Probabilities 3 probability to have at least 1 item : 18% of 1565 lgs > 286 lgsprobability to have at least 2 items : 19% ~ 27% of 286 lgs > 55 ~ 79 lgsprobability to have at least 3 items : 29% ~ 37% of 55 ~ 79 lgs > 16 ~ 29 lgs

  22. Some questions... • Can this method be used to classify a language ? • What is the minimal number of items needed to identify a language cluster ? • Is there a method (other than intuitive) to identify these items ? • Can this technique be applied to any language family / cluster ? • What are the implications of these phenomena ? • ...

  23. A restricted distribution **GOP : possible Atlantic lexical innovation

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