APA 6th Edition Sepesy Maučec, M. i Kačić, Z. (2007). Statistical Machine Translation from Slovenian to English. Journal of computing and information technology, 15 (1), 47-59. https://doi.org/10.2498/cit.1000760
MLA 8th Edition Sepesy Maučec, Mirjam i Zdravko Kačić. "Statistical Machine Translation from Slovenian to English." Journal of computing and information technology, vol. 15, br. 1, 2007, str. 47-59. https://doi.org/10.2498/cit.1000760. Citirano 02.03.2021.
Chicago 17th Edition Sepesy Maučec, Mirjam i Zdravko Kačić. "Statistical Machine Translation from Slovenian to English." Journal of computing and information technology 15, br. 1 (2007): 47-59. https://doi.org/10.2498/cit.1000760
Harvard Sepesy Maučec, M., i Kačić, Z. (2007). 'Statistical Machine Translation from Slovenian to English', Journal of computing and information technology, 15(1), str. 47-59. https://doi.org/10.2498/cit.1000760
Vancouver Sepesy Maučec M, Kačić Z. Statistical Machine Translation from Slovenian to English. Journal of computing and information technology [Internet]. 2007 [pristupljeno 02.03.2021.];15(1):47-59. https://doi.org/10.2498/cit.1000760
IEEE M. Sepesy Maučec i Z. Kačić, "Statistical Machine Translation from Slovenian to English", Journal of computing and information technology, vol.15, br. 1, str. 47-59, 2007. [Online]. https://doi.org/10.2498/cit.1000760
Sažetak In this paper, we analyse three statistical models for the machine translation of Slovenian into English. All of them are based on the IBM Model~4, but differ in the type of linguistic knowledge they use. Model 4a uses only basic linguistic units of the text, i.e., words and sentences. In Model 4b, lemmatisation is used as a preprocessing step of the translation task. Lemmatisation also makes it possible to add a Slovenian-English dictionary as an additional knowledge source. Model 4c takes advantage of the morpho-syntactic descriptions (MSD) of words. In Model 4c, MSD codes replace the automatic word classes used in Models 4a and 4b. The models are experimentally evaluated using the IJS-ELAN parallel corpus.