Architecture for automatic poetry generation through pattern recognition

Document representation and topic modelling are important problems for artificial intelligence researchers, with applications ranging from education technology to bioinformatics. Many approaches have been proposed, the majority falling broadly into categories of Statistical Analysis and Natural Language Processing (NLP). This thesis proposes an architecture that optimizes a combination of statistical and linguistic analysis in an unsupervised machine learning environment. The proposed architecture is a design for agile, stable, document modelling. By clustering within the statistical inference algorithm, it reduces the computational cost of time and space associated with conventional classifying algorithms such as K-means, increasing the threshold for size and frequency of aggregate data analysis. This translates to an increased stability for evolution of learning. The architecture builds on the concept of socio-linguistic connections as an inherent combination of statistics and linguistics, and employs well-researched concepts of statistical and linguistic analysis, including embedded sub-manifold analysis. ...