Choosing the correct paradigm for unknown words in rule-based machine translation systems

Abstract

Previous work on an interactive system aimed at helping non-expert users to enlarge the monolingual dictionaries of rule-based machine translation (MT) systems worked by discarding those inflection paradigms that cannot generate a set of inflected word forms validated by the user. This method, however, cannot deal with the common case where a set of different paradigms generate exactly the same set of inflected word forms, although with different inflection information attached. In this paper, we propose the use of an n-gram-based model of lexical categories and inflection information to select a single paradigm in cases where more than one paradigm generates the same set of word forms. Results obtained with a Spanish monolingual dictionary show that the correct paradigm is chosen for around 75% of the unknown words, thus making the resulting system (available under an open-source license) of valuable help to enlarge the monolingual dictionaries used in MT involving non-expert users without technical linguistic knowledge.