Cel Since roughly a decade statistical machine translation (SMT) predominates in academic research. However, most commercial MT suppliers continue to offer systems based on more traditional rule-based architectures (RBMT). Difficulties with replacing the translation engines in the product set-up may explain this discrepancy in part. However, the main reasons are that RBMT makes available a whole bunch of functions which SMT does not provide, including human-readable, fully worked out 'conventional' dictionaries, and that for a number of language pairs RBMT-quality is still higher.SMT needs huge bilingual text corpora to compute satisfactory translation models, and it is inherently weak when dealing with rare data and non-local phenomena. Its advantages are low cost and robustness. The main disadvantages of RBMT are high cost and shortcomings with respect to resolving structural and lexical ambiguities.We propose a hybrid architecture for high quality machine translation which combines the strengths of both approaches and minimizes their weaknesses: At the core is a rule-based MT system which provides morphology, declarative grammars, semantic categories, and small dictionaries, but which avoids all expensive kinds of intellectual knowledge acquisition. Instead of manually working out large dictionaries and compiling information on disambiguation preference, we suggest a novel corpus-based bootstrapping method for automatically expanding dictionaries, and for training the analytical performance and the choice of transfer alternatives.As bilingual corpora with good literal translations are a sparse resource, we focus in particular on exploiting comparable monolingual corpora. We locate unknown words and expressions, and then use a statistically tuned analysis component in combination with similarity assumptions to identify relations across languages. This approach should make it possible to overcome the data acquisition bottleneck of conventional SMT. Dziedzina nauki humanitieslanguages and literaturegeneral language studies Program(-y) FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Temat(-y) FP7-PEOPLE-2009-IAPP - Marie Curie Action: "Industry-Academia Partnerships and Pathways" Zaproszenie do składania wniosków FP7-PEOPLE-2009-IAPP Zobacz inne projekty w ramach tego zaproszenia System finansowania MC-IAPP - Industry-Academia Partnerships and Pathways (IAPP) Koordynator UNIVERSITY OF LEEDS Wkład UE € 571 811,00 Adres WOODHOUSE LANE LS2 9JT Leeds Zjednoczone Królestwo Zobacz na mapie Region Yorkshire and the Humber West Yorkshire Leeds Rodzaj działalności Higher or Secondary Education Establishments Kontakt administracyjny Keri Dunning (Ms.) Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Koszt całkowity Brak danych Uczestnicy (1) Sortuj alfabetycznie Sortuj według wkładu UE Rozwiń wszystko Zwiń wszystko LINGENIO GMBH Niemcy Wkład UE € 261 382,00 Adres KARLSRUHER STRASSE 10 69126 HEIDELBERG Zobacz na mapie Rodzaj działalności Private for-profit entities (excluding Higher or Secondary Education Establishments) Kontakt administracyjny Kurt Eberle (Dr.) Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Koszt całkowity Brak danych