The first morphological learner based upon the theory of Whole Word Morphology (Ford et al., 1997 ) is outlined, and preliminary evaluation results are presented. The program, Whole Word Morphologizer, takes a POS-tagged lexicon as input, induces morphological relationships without attempting to discover or identify morphemes, and is then able to generate new words beyond the learning sample. The accuracy (precision) of the generated new words is as high as 80% using the pure Whole Word theory, and 92% after a post-hoc adjustment is added to the routine. The aim of this project is to develop a computational model employing the theory of whole word morphology (Ford et al., 1997) capable on the one hand of identifying morphological relations within a list of words from any one of a wide variety of languages and, on the other, of putting that knowledge to use in creating previously unseen word forms. A small application called Whole Word Morphologizer which does just this is outlined and discussed. In particular, this approach is set against the literature on computational morphology as an entirely different way of doing things which has the potential to be generalized to all known varieties of morphology in the world's languages, a feature not shared by previous methods. As it is based on a model of the mental lexicon in which all entries are entire, fully fledged words, this project also serves as an empirical demonstration that a word-based morphological theory that rejects the notion of morpheme as minimal unit of form and meaning (and/or grammatical properties) is viable from the point of view of acquisition as well as generation. Since its inception in the mid 1950s, the field of computational morphology has been characterized by a paucity of procedures for generation. Notwithstanding the impressive body of literature on the shortcomings of traditional Paninian morphology, most computational research projects also rely on a traditional notion of the morpheme and ignore all non-compositional aspects of morphology. These observations are obviously not unrelated and are in part inherited from the field of computational syntax where applications traditionally were designed to assign a syntactic structure to a given string of words, though this is less true today. Word formation and the population of the lexicon, while central to morphological theory, are noticeably absent from the field of computational morphology. Most computational work in the field of morphology has focused on the identification of morphemes or morphological parsing while paying little or no attention to generation. While these applications find a common goal in the automatic acquisition of morphology, it is helpful to distinguish between two types of analysis in light of the often very different results sought by various morphological learners. On the one hand, some applications focus exclusively on the segmentation of words or longer strings into smaller units. In other words, their
The Whole Word Morphologizer REF ) uses a POS-tagged lexicon as input, induces morphological relationships without attempting to discover or identify morphemes.
Unsupervised Learning Of Morphology Without Morphemes
Workshop On Morphological And Phonological Learning
Computer Science