This thesis is based on the assumption that phonetic (or graphemic) similarity mea-sures facilitate the induction of word mappings between cognate words. Cognate words appear most frequently in closely related languages. Moreover, the phonetic models rely on word lists only; they did not need more sophisticated data like parallel
corpora. Thus, phonetic similarity models are ideal for closely related language pairs with (at least) one scarce-resource language. However, it is clear that these models, working on limited data, cannot perform as well as models working on more informa-tive data. We showed that even a small parallel corpus yields better lexicon mappings than those obtained with phonetic similarity measures on simple word lists.
We have proposed ten models that encode phonetic similarity in different manners and grouped them into three categories – static models, models of direct adaptation, and models of indirect adaptation. We showed that adaptive models perform better than static ones, provided that enough data is available to adapt the models to the lan-guage pair. The information for the adaptation can be presented in the form of a lin-guistic description of the phoneme transformations, like in the case of the rule-based model, or as a list of reference word pairs in the case of the learning models. Further-more, we found that the models which take the phonetic context into account, like the rule-based model or then-gram models, perform better than simpler ones, based on the comparison of single letters. However, even the models that rely on phoneme contexts still induce every word pair in an isolated way. Hence, these models may stitute a basis for a more complex framework that takes into account the syntactic con-texts of the word pairs to be induced.
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