As argued in chapter 1, if a natural language processing task is formulated as either a disambiguation task or a segmentation task, it can be presented as a classification task to a memory-based learner, as well as to any other machine learning algorithm capable of learning from labeled examples. In this chapter as well as in the next we provide examples of how we formulate tasks in an MBLP framework. We start with one disambiguation and one segmentation task operating at the phonological and morphological levels, respectively.

A non-trivial portion of the complexity of natural languages is determined at the phonological and morphological levels, where phonemes and morphemes come together to form words. A language's phoneme inventory is based on many individual observations in which changing one particular speech sound of a spoken word into another changes the meaning of the word. A morpheme is usually identified as a string of phonemes carrying meaning on its own; a special class of morphemes, affixes, does not carry meaning on its own, but instead affixes have the ability to add or change some aspect of meaning when attached to a morpheme or string of morphemes.

One major problem of natural language processing in the phonological and morphological domains is that many existing sequences of phonemes and morphemes have highly ambiguous surface written forms, and especially in alphabetic writing systems where there is ambiguity in the relation between letters and phonemes.