In the IBM LMT Machine Translation (MT) system, a built-in strategy provides lexical coverage of a particular subset of words that are not listed in its bilingual lexicons. The recognition and coding of these words and their transfer generation is based on a set of derivational morphological rules. A new utility extends unfound words of this type in an LMT-compatible format in an auxiliary bilingual lexical file to be subsequently merged into the core lexicons. What characterizes this approach is the use of morphological, semantic, and syntactic features for both analysis and transfer. The auxiliary lexical file (ALF) has to be revised before a merge into the core lexicons. This utility integrates a linguistics-based analysis and transfer rules with a corpus-based method of verifying or falsifying linguistic hypotheses against extensive document translation, which in addition yields statistics on frequencies of occurrence as well as local context.

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 structures, and the creation of transfers in the transformation component. We also successfully applied rules of derivational morphological analysis to non-derived unfound words.