Sem field | 1937 1970 1995 | change | comment ====================================================== Sport | 1 1 1 | - | Marine | 2 2 7 | \ | ! Culture | 3 9 4 | \/ | Economy | 4 3 8 | /\ | ? Clothing | 5 8 3 | \/ | Food | 6 7 5 | \/ | Transport | 7 5 9 | /\ | Sci & tech | 8 4 6 | /\ | ? Agriculture | 9 11 11 | \ | People | 10 6 2 | / | ! Politics | 11 10 10 | / | ?
In the above table, three middle columns correspond to the results of the studies on semantics of English loan words in Polish conducted by Koneczna (1937), Fisiak (1970) and Manczak-Wohlfeld (1995). In each of these columns, the smaller the ordinal number, the greater the number of borrowings in a given semantic field
In the table, the semantic classification adopted for all three studies was the one from Fisiak 1970. Since the original semantic categories in the other two studies differed from Fisiak's categories, the ordering in the first and third column are only approximate.
In particular, if we treated different Manczak-Wohlfeld's categories for different sciences and technologies (not included in the table) as one 'science & technology' category, we would probably get the most numerous category of all.
This claim seems to be supported by my recent study of neologies in Polish computer texts (i.e. computer magazines, web pages, etc), where approx. 1800 computer-related terms borrowed from English were attested. Compared to the total of 1700 borrowings found by Manczak-Wohlfeld, the semantic field of 'science and technology' would outnumber any other semantic field she considered.
It should be also noted, that Manczak-Wohlfeld did not
(could not) consider vocabulary connected with European Union and
NATO borrowed recently into Polish from English
(opting out, opting in, accountability, pillars, quota-hopping, etc).
For this reason, in a more complete (up-to-date) study, the semantic field
of politics and economy would probably rank higher than here.
POS | Freq. ============================ nouns | 94.2% verbs | 2.7% adjectives | 2.4% adverbs | 0.6% exclamations | 0.2%Explanations:
Noun gender | Freq. ============================ masculine | 76.5% feminine | 8.2% neutral | 5.3% no (fixed) gender | 0.6%Explanations: