“Faux amis” or “false friends” (abbreviated hereafter as FF) are metalinguistic terms used to name word pairs that look alike in two languages, but that do not have the same meaning. The purpose of this paper is to identify and classify FF in a corpus of medical English prose. Our source material consists of twenty research papers randomly chosen and published between 1994 and 1996 in various leading medical journals. Every FF was identified by means of a contextual analysis. Latest Checkpoint Vpn 1 Edge X Firmware Download - Free Torrent more. The deceptive cognates recorded were classified according to two groups of variables: FF types and word classes.

The proportion of FF found in the corpus reached 5.3% of the number of total running words. This leads us to believe that they deserve special attention since it is well known that they represent an important problem for terminologists and are frequently misinterpreted by medical doctors and students. We thus believe that the elaboration of tables containing word pairs (English-Spanish, in our case) most frequently encountered, along with well designed exercises, could prove most helpful if presented at an early stage of reading comprehension and translation courses. 17 Table 3 and Figure 3 illustrate that the 387 FF lexemes gave 4,789 occurrences in the 89,688 running words. The proportion of those occurrences in the biomedical corpus then reached 5.3%.

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One can thus infer that in a 6,000-word-research paper (the length of about half of those analysed in our corpus), a reader may encounter about 318 FF forms, or an average of 159 if the article were 3,000 words long, which means more than five deceptive cognates in every 100 running words of text). This may imply a considerable risk of misinterpretation of the original article. Distribution of FF types in word classes (number of lexemes) FF Nouns Adjectives Verbs Adverbs T 44 (24.17%) 17 (18.47%) 8 (8.33%) 4 (23.52%) P1 33 (18.13%) 14 (15.21%) 13 (13.54%) 2 (11.76%) P2 105 (57.69%) 61 (66. Dell Optiplex 170l Sound Driver Free Download on this page. 30%) 75 (78.12%) 11 (6470%) FINAL NUMBER 182 (100%) 92 (100%) 96 (100%) 17 (100%). 18 As we can see in Table 4 and Figure 4, among the 387 FF lexemes identified in the corpus, the number of adjectives and verbs was similar, while there were about twice as many nouns, and only a few adverbs. With regard to the three types of FF, nouns accounted for roughly a 60% of the total number of FF lexemes. Beachfront Riddim Rar Extractor. Distribution of FF types in word classes (number of occurrences) FF Nouns occ. Adjectives occ.