Probabilistic Document Modelling

In this thesis the development and application of probabilistic models of documents is considered. The initial focus is on language models which provide a way of modelling plain text documents. In particular the hierarchical Dirichlet language model, which is derived from simple Bayesian theory, is investigated and is shown to be well approximated by an existing method known as generalised PPM-A. Using this equivalence, generalised PPM-A is extended to produce a language model which while working on the level of individual letter-like symbols is able to make use of the division of the text stream into words. It is shown that the new model can be used in conjunction with a word list to improve performance when very little information from which to learn the statistics of the language is available. The hierarchical Dirichlet model is then applied to the task of information retrieval, producing a new retrieval method which naturally includes document frequency information. This information has traditionally been used in retrieval systems, but previously had either been missing or introduced heuristically in language model based approaches to the problem. The hierarchical approach is also extended to the task of retrieval at the passage level where it is shown to give promising results. Finally, the scope of the investigation is broadened to include documents which contain diagrams as well as plain text. A method is developed to group fragments of digitised ink strokes into perceptually relevant components of a diagram, while at the same time labelling the components with an object class. The approach, which is based on the conditional random field, is shown to work well both in terms of grouping and improving labelling performance when compared to other methods.