he field of statistical natural language processing has been turning toward morphologically rich languages. These languages have vocabularies that are often orders of magnitude larger than that of English, since words may be inflected in various different ways. This leads to problems with data sparseness and calls for models that can deal with this abundance of related words—models that can learn, analyze, reduce and generate morphological inflections. But surprisingly, statistical approaches to morphology are still rare, which stands in contrast to the many recent advances of sophisticated models in parsing, grammar induction, translation and many other areas of natural language processing.
This thesis presents a novel, unified statistical approach to inflectional morphology, an approach that can decode and encode the inflectional system of a language. At the center of this approach stands the notion of inflectional paradigms. These paradigms cluster the large vocabulary of a language into structured chunks; inflections of the same word, like break, broke, breaks, breaking, … , all belong in the same paradigm. And moreover, each of these inflections has an exact place within a paradigm, since each paradigm has designated slots for each possible inflection; for verbs, there is a slot for the first person singular indicative present, one for the third person plural subjunctive past and slots for all other possible forms. The main goal of this thesis is to build probability models over inflectional paradigms, and therefore to sort the large vocabulary of a morphologically rich language into structured clusters. These models can be learned with minimal supervision for any language that has inflectional morphology. As training data, some sample paradigms and a raw, unannotated text corpus can be used.
The models over morphological paradigms are developed in three main chapters that start with smaller components and build up to larger ones.
The first of these chapters (Chapter 2) presents novel probability models over strings and string pairs. These are applicable to lemmatization or to relate a past tense form to its associated present tense form, or for similar morphological tasks. It turns out they are general enough to tackle the popular task of transliteration very well, as well as other string-to-string tasks.
The second (Chapter 3) introduces the notion of a probability model over multiple strings, which is a novel variant of Markov Random Fields. These are used to relate the many inflections in an inflectional paradigm to one another, and they use the probability models from Chapter 2 as components. A novel version of belief propagation is presented, which propagates distributions over strings through a network of connected finite-state transducers, to perform inference in morphological paradigms (or other string fields).
Finally (Chapter 4), a non-parametric joint probability model over an unannotated text corpus and the morphological paradigms from Chapter 3 is presented. This model is based on a generative story for inflectional morphology that naturally incorporates common linguistic notions, such as lexemes, paradigms and inflections. Sampling algorithms are presented that perform inference over large text corpora and their implicit, hidden morphological paradigms. We show that they are able to discover the morphological paradigms that are implicit in the corpora. The model is based on finite-state operations and seamlessly handles concatenative and nonconcatenative morphology.