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Simpozij OBDOBJA34 (cf. Covington 2010: 96). Because of the moderate text length in our data we choose a windowsize of 100. The output5 shows the following ratios: Table 2: MATTR of the subcorpora. Window size = 1006 Subcorpus Tokens MATTR [olar 7620 0.603 VSHermagoras2012/13a 1989 0.630 VSHermagoras2012/13b 2313 0.621 VSHermagoras2012/13c 1635 0.597 VSHermagoras2012/13d 1042 0.566 VSHermagoras2012/13e 1331 0.550 VSHermagoras2011/12 3219 0.633 The highest values in the TTR and thus the least repetitions of single words are shown by three Austrian-Carinthian groups, whereas the pupils from Slovenia are ranked fourth. How is this possible? The reason is to be sought in the distribution of high frequency lemmata. Let us take verbs as an example.7 A closer look at the most frequent verbs reveals why the VS 11/12 texts show the highest value – and not the moreadvanced[olar subcorpus: Table 3: Verbs in the subcorpora Verbs count Verbs VScount Verbs VScount Verbs VS count [olar 12/13a 12/13b 12/13c biti 1319 biti 322 biti 406 biti 285 iti 46 iti 20 imeti 40 iti 32 priti 46 imeti 14 iti 29 imeti 16 povedati 33 priti 12 peljati 19 videti 14 dati 33 videti 11 hoteti 12 narediti 12 imeti 30 peljati 10 dati 10 priti 9 oditi 30 igrati 9 videti 10 igrati 8 za~eti 27 re~i 8 re~i 9 peljati 7 vpra{ati 25 jesti 6 pasti 9 de`evati 5 dobiti 23 za~eti 6 vzeti 8 te~i 5 5 Generated by Covington & Fall’s MATTR – A CASPR project. http://ai1.ai.uga.edu/caspr/ MATTR2.zip 6 There is no consensus as to the margin of what the MATTR of a given text should reach. At a Window size of 500, Kettunen (2014) analysed the EU Constitution and discovered a MATTR between 0.39 and 0.60 depending on language (Slovene: 0.53). He did the same for parts of the Leipzig corpus (which contains randomly selected sentences from newspapers and web pages of different languages, http://corpora.uni-leipzig.de/download.html) and obtained a MATTR between 0.61 and 0.86 (Slovene: 0.73). Furthermore, the authors of the MATTR considered a value of 2 W-0.02 for any English text. 7 Like conjunctions, verbs are indicators of proficiency, since the formation of sentences depends on the use of verbs. A good command of verb lexemes is thus the basis for expressing a variety of states. 177