Advances in Cognitive Sociolinguistics (Cognitive Linguistic Research)

Lexical convergence and divergence in Portuguese 49

nal uniformity is greater in the 1950s Portuguese database (I 55.8%) than in
the 1950s Brazilian database (I 38.1%). This can be explained by the two
factors which contribute to determine internal uniformity. First, P50 has a
single term which is clearly dominant whereas B50 has two dominant
terms. Second, there are more highly frequent alternative terms in B50 than
in P50.
The proportion of terms possessing a special feature, or A measure, is
given in the following formulae. As with the U and U’ measures, the A’
measure takes into account the relative frequency of each concept whereas
the A measure does not.

A K,Z (Y) = F Z,Y (Xi). WXi (K)

The proportion A of all items x with feature K in the onomasiological pro-
file of a concept Z in the subcorpus Y equals the sum of x’s relative fre-
quencies weighted by the membership value W.

A’K (Y) = A K,Zi (Y). GZi (Y)

The proportion A’ of all items x with feature K in the subcorpus Y equals
the sum of all A-measures, weighted by G, that is the relative frequency of
concept Z in Y.

Table 3. The impact of the English loans (A) on the GOAL 1 profile in EP and BP in
the 1950s

GOAL 1

P50 B50

abs rel rel*W abs rel rel*W W

bola 109 3,7 0,0 0 0,0 0,0 0

goal 24 0,8 0,8 528 38,8 38,8 1

gol 0 0,0 0,0 111 8,1 4,1 0,5

gôl 0 0,0 0,0 66 4,8 1,9 0,4

golo 1841 61,9 31,0 0 0,0 0,0 0,5

ponto 204 6,9 0,0 26 1,9 0,0 0

tento 795 26,7 0,0 631 46,3 0,0 0

31,8 44,8

Assigning a special feature K is not a binary issue, but rather the result of a
continuum. If we consider the loanword feature, for example, the highest

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