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U radu se prikazuju rezultati terenskoga istraživanja o pomicanju naglasaka u imenskih riječi na prednaglasnicu, odnosno o praslavenskom prijenosu siline unutar naglasne cjeline u čakavskome govoru Crikvenice. Pomicanje se naglasaka na prednaglasnicu provodi dosljedno u svima razmatranim kategorijama (osim u brojeva) u kojima su zadovoljeni ovi uvjeti: prvotni silazni naglasak na prvome (ili jedinome) slogu osnove one riječi koja čini naglasnu cjelinu s prijedlogom ispred sebe. U akuzativu su imenica muškoga i ženskoga roda zabilježene alternacije. Pomicanje naglasaka na prednaglasnicu u nekim primjerima srednjega roda u L jd. zahvaća i drugotne silazne naglaske koji su se na prvome slogu osnove našli naknadno, zbog naglasnoga ujednačavanja paradigme ili naglasnoga tipa.
Part-of-Speech tagging is generally performed by Markov models, based on bigram or trigram models. While Markov models have a strong concentration on the left context of a word, many languages require the inclusion of right context for correct disambiguation. We show for German that the best results are reached by a combination of left and right context. If only left context is available, then changing the direction of analysis and going from right to left improves the results. In a version of MBT (Daelemans et al., 1996) with default parameter settings, the inclusion of the right context improved POS tagging accuracy from 94.00% to 96.08%, thus corroborating our hypothesis. The version with optimized parameters reaches 96.73%.
In this paper we compare the distribution of PPs introducing external arguments in nominalizations with PPs introducing external arguments in the verbal domain. We show that several mismatches exist between the behavior of PPs in nominalizations and PPs in the verbal domain. This leads us to suggest that while PPs in the verbal domain are licensed by functional structure alone, within the nominal domain, PPs can also be licensed via an interplay of the encyclopaedic meaning of the root involved and the properties of the preposition itself. This second mechanism kicks in in the absence of functional structure.
U radu se analiziraju pravi tvorbeni mocijski parnjaci u kajkavskome književnom jeziku. Utvrđuju se sufiksi produktivni u mocijskoj tvorbi u književnoj kajkavštini, njihova učestalost i korelativni parnjaci u kojima se javljaju. Rezultati se uspoređuju s osobitostima mocijske tvorbe u hrvatskome standardnom jeziku.
Die Entwicklung eines individuellen Standards „vom grünen Tisch“ führt selten zu zufriedenstellenden Ergebnissen. Bei der automatischen Prüfung stellt man schnell fest, dass die „ausgedachten“ Regeln einer systematischen Anwendung nicht standhalten. Bei der Implementierung solcher Richtlinien stellt man fest, dass sie oft zu wenig konkret formuliert sind, wie z.B. „formulieren Sie Handlungsanweisungen knapp und präzise“. Wie jedoch kann ein Standard entwickelt werden, der zu einem Unternehmen, seiner Branche und Zielgruppen passt und für die automatische Prüfung implementiert werden kann? Sprachtechnologie hilft effizient bei der Entwicklung individueller Richtlinien. Durch Datenanalyse, Satzcluster und Parametrisierung entsteht ein textspezifischer individueller Standard. Ist damit aber der Gegensatz von Kreativität und Standardisierung aufgehoben?