Computing and Information Systems - Theses
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ItemImproving the utility of topic models: an uncut gem does not sparkle( 2013)This thesis concerns a type of statistical model known as topic model. Topic modelling learns abstract “topics” in a collection of documents, and by “topic” we mean an idea, theme or subject. For example we may have an article that discusses space exploration, or a book about crime. Space exploration and crime, these two subjects, are the “topics” that we are talking about. As one imagine, topic modelling has a direct application in digital libraries, as it automates the learning and categorisation of topics in books and articles. The merit of topic modelling, however, is that its machinery is not limited to processing just words but symbols in general. As such, topic modelling has seen applications in other areas outside text processing such as biomedical research for inferring protein families. Most applications, however, are small scale and experimental and much of the impact is still contained in academic research. The overarching theme of the thesis is thus to improve the utility of topic modelling. We achieve this in two ways: (1) by improving a few aspects of topic modelling to make it more accessible and usable by users; and (2) by proposing novel applications of topic modelling to real-world problems. In the first step, we look into improving the preprocessing methodology of documents that serves as the creation of input for topic models. We also experiment extensively to improve the visualisation of topics—one of the main output of topic models—to increase its usability for human users. In the second step, we apply topic modelling in a lexicography-oriented work to learn and detect new meanings that have emerged in words and in the social media space to identify popular social trends. Both were novel applications and delivered promising results, demonstrating the strength and wide applicability of topic models.
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ItemCollective document classification using explicit and implicit inter-document relationships( 2013)Information systems are transforming the ways in which people generate, store and share information. One consequence of this change is a massive increase in the quantity of digital content the average person needs to deal with. A large part of the information systems challenge is about finding intelligent ways to help users locate and analyse this information. One tool that is available to build systems to address this challenge is automatic document classification. A document classifier is a statistical model for predicting a label for an input document that is represented as a set of features. The potential usefulness of such a generalised system for categorising documents based on their contents is very great. There are direct applications for systems that can answer complex document categorisation questions like: Is this product review generally positive or negative? Document classification systems can also become critical parts of most complex systems that need input documents to be selected based on complex criteria. This thesis addresses the question of how document classifiers can exploit information about the relationships between documents being classified. Normally, document classifiers work on a single document at a time: once the classifier has been trained from a set of labelled examples, it can then be used to label single input documents as required. Collective document classifiers learn a classifier that can be applied to a group of related documents. The inter-document relationships in the group are used to improve labelling performance beyond what is possible when considering documents in isolation. Work on collective document classifiers is based on the observation that some types of documents have features which are either ambiguous or not present in training data, but which have the special characteristic of indicating relationships between the labels of documents. Most often, an inter-document relationship indicates that two documents have the same label, but it may also indicate that they have different labels. In either case, classifiers gain an advantage if they can consider inter-document features. Inter-document features can be explicit, as when a document cites or quotes another, or implicit, as when documents exist in semantically related groups in which stylistic, structural or semantic similarities are informative, or when they are related by a spatial or temporal structure. In the first part of this thesis I survey the state-of-the-art in collective document classification and explore approaches for adding collective behaviour to standard document classifiers. I present an experimental evaluation of these techniques for use with explicit inter-document relationships. In the second part I develop techniques for extracting implicit inter-document relationships. In total, the work in this thesis assesses and extends the capabilities of collective document classifiers. Its contribution is in four main parts: (1) I introduce an approach that gives better than state of the art performance for collective classification of political debate transcripts; (2) I provide a comparative overview of collective document classification techniques to assist practitioners in choosing an algorithm for collective document classification tasks; (3) I demonstrate effective and novel approaches for generating collective classifiers from standard classifiers; and (4) I introduce a technique for inferring inter-document relationships based on matching phrases and show that these relationships can be used to improve overall document classification performance.