Machine Translation (MT) systems that process unrestricted text should be able to deal with words that are not found in the MT lexicon. Without some kind of recognition, the parse may be incomplete, there is no transfer for the unfound word, and tests for transfers for surrounding words will often fail, resulting in poor translation. Interestingly, not much has been published on unfound-word guessing in the context of MT although such work has been going on for other applications. In our work on the IBM MT system, we implemented a far-reaching strategy for recognizing unfound words based on rules of word formation and for generating transfers. What distinguishes our approach from others is the use of semantic and syntactic features for both analysis and transfer, a scoring system to assign levels of confidence to possible word structum, and the creation of transfers in the transformation component. We also successfully applied rules of derivational morphological analysis to non-derived unfound words.
By: Claudia Gdaniec, Esme Manandise, Michael C. McCord
Published in: RC22029 in 2001
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