A new hybrid approach enabling MT for languages with little resources

In this paper, we combine techniques from rule-based and corpus-based MT in a hybrid approach. We only use a dictionary, basic analytical resources and a monolingual targetlanguage corpus in order to enable the construction of an MT system for lesser-resourced languages. Statistical and example-based systems usually do not involve a lot of linguistic notions. Cutting up sentences in linguistically sound subunits improves the quality of the translation. Demarcating clauses, verb groups, noun phrases, and prepositional phrases restricts the number of possible translations and hence also the search space. The sentence chunks are translated using a dictionary and a limited set of mapping rules. By bottom-up matching the different translated items and higher-level structure with the database information, one or more plausible translated sentences are constructed. A search engine ranks them using the frequencies of occurence and the matching accuracy in the target-language corpus.