2004. Induction of a simple morphology for highly-inflecting languages

This paper presents an algorithm for the unsuper-vised learning of a simple morphology of a nat-ural language from raw text. A generative prob-abilistic model is applied to segment word forms into morphs. The morphs are assumed to be gener-ated by one of three categories, namely prefix, suf-fix, or stem, and we make use of some observed asymmetries between these categories. The model learns a word structure, where words are allowed to consist of lengthy sequences of alternating stems and affixes, which makes the model suitable for highly-inflecting languages. The ability of the al-gorithm to find real morpheme boundaries is eval-uated against a gold standard for both Finnish and English. In comparison with a state-of-the-art al-gorithm the new algorithm performs best on the Finnish data, and on roughly equal level on the En-glish data.