1. 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

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 1TU(P) : to spit 2MED ~ MOD : to swallow 3NYU : to drink 4DUM : to bite 5TE : tree 6NYI(N) : tooth 7TU : ear 8DEM : tongue 9DI : to eat 10TAT : 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 tolerance I~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 lgs probability to have at least 2 items : 19% ~ 27% of 286 lgs > 55 ~ 79 lgs probability 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