Zugl.: Bielefeld, Univ., Diss., 2009

Topic models are a new research field within the computer sciences information retrieval and text mining. They are generative probabilistic models of text corpora inferred by machine learning and they can be used for retrieval and text mining tasks. The most prominent topic model is latent Dirichlet allocation (LDA), which was introduced in 2003 by Blei et al. and has since then sparked off the development of other topic models for domain-specific purposes. This thesis focuses on LDA's practical application. Its main goal is the replication of the data analyses from the 2004 LDA paper ``Finding scientific topics'' by Thomas Griffiths and Mark Steyvers within the framework of the R statistical programming language and the R~package topicmodels by Bettina Grün and Kurt Hornik. The complete process, including extraction of a text corpus from the PNAS journal's website, data preprocessing, transformation into a document-term matrix, model selection, model estimation, as well as presentation of the results, is fully documented and commented. The outcome closely matches the analyses of the original paper, therefore the research by Griffiths/Steyvers can be reproduced. Furthermore, this thesis proves the suitability of the R environment for text mining with LDA. (author's abstract) / Series: Theses / Institute for Statistics and Mathematics

This thesis explores the ideas of feature learning and sparse coding for Music Information Retrieval (MIR). Sparse coding is an algorithm which aims to learn new feature representations from data automatically. In contrast to previous work which uses sparse coding in an MIR context the concept of supervised sparse coding is also investigated, which makes use of the ground-truth labels explicitly during the learning process. Here sparse coding and supervised coding are applied to two MIR problems: classification of musical genre and recognition of the emotional content of music. A variation of Label Consistent K-SVD is used to add supervision during the dictionary learning process. In the case of Music Genre Recognition (MGR) an additional discriminative term is added to encourage tracks from the same genre to have similar sparse codes. For Music Emotion Recognition (MER) a linear regression term is added to learn an optimal classifier and dictionary pair. These results indicate that while sparse coding performs well for MGR, the additional supervision fails to improve the performance. In the case of MER, supervised coding significantly outperforms both standard sparse coding and commonly used designed features, namely MFCC and pitch chroma.

This dissertation examines the impact of exploration and learning upon eDiscovery information retrieval; it is written in three parts. Part I contains foundational concepts and background on the topics of information retrieval and eDiscovery. This part informs the reader about the research frameworks, methodologies, data collection, and instruments that guide this dissertation. Part II contains the foundation, development and detailed findings of Study One, "The Relationship of Exploration with Knowledge Acquisition." This part of the dissertation reports on experiments designed to measure user exploration of a randomly selected subset of a corpus and its relationship with performance in the information retrieval (IR) result. The IR results are evaluated against a set of scales designed to measure behavioral IR factors and individual innovativeness. The findings reported in Study One suggest a new explanation for the relationship between recall and precision, and provide insight into behavioral measures that can be used to predict user IR performance. Part II also reports on a secondary set of experiments performed on a technique for filtering IR results by using "elimination terms." These experiments have been designed to develop and evaluate the elimination term method as a means to improve precision without loss of recall in the IR result. Part III contains the foundation, and development of Study Three, "A New System for eDiscovery IR Based on Context Learning and Relevance." This section reports on a set of experiments performed on an IT artifact, Legal Intelligence®, developed during this dissertation. The artifact developed for Study Three uses a learning tool for context and relevance to improve the IR extraction process by allowing the user to adjust the IR search structure based on iterative document extraction samples. The artifact has been developed based on the needs of the business community of practitioners in the domain of eDiscovery; it has been instantiated and tested during Study Three and has produced significant results supporting its feasibility for use. Part III contains conclusions and steps for future research extending beyond this dissertation.

The vision of pervasive computing is one of a personalized space populated with vast amounts of data that can be exploited by humans. Such Personalized Networked Spaces (PNetS) and the requisite support for general-purpose expressive spatiotemporal search of the “here” and “now” have eluded realization, due primarily to the complexities of indexing, storing, and retrieving relevant information within a vast collection of highly ephemeral data. This thesis presents the Gander search engine, founded on a novel conceptual model of search in PNetS and targeted for environments characterized by large volumes of highly transient data. We overview this model and provide a realization of it via the architecture and implementation of the Gander search engine. Gander connects formal notions of sampling a search space to expressive, spatiotemporal-aware protocols that perform distributed query processing in situ. This thesis evaluates Gander through a user study that examines the perceived usability and utility of our mobile application, and benchmarks the performance of Gander in large PNetS through network simulation. / text

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

This thesis investigates automatic geolocation (i.e. identification of the location, expressed as latitude/longitude coordinates) of documents. Geolocation can be an effective means of summarizing large document collections and is an important component of geographic information retrieval. We describe several simple supervised methods for document geolocation using only the document’s raw text as evidence. All of our methods predict locations in the context of geodesic grids of varying degrees of resolution. We evaluate the methods on geotagged Wikipedia articles and Twitter feeds. For Wikipedia, our best method obtains a median prediction error of just 11.8 kilometers. Twitter geolocation is more challenging: we obtain a median error of 479 km, an improvement on previous results for the dataset. / text

Categorizing knowledge into different disciplines and units may block knowledge within separate “folders”, which could limit its later retrieval and transfer to new contexts. To test this hypothesis, two experiments had been conducted. In one experiment, participants memorized a list of words with or without cuing which category these words belonged to. One week later, they were asked to recall all the positive adjectives, which required them to retrieve words that came from different categories. In the other experiment, participants read exactly the same story but embedded in two different subject domains or no context. A survey report was presented to test whether people from different contexts would have different transfer effect. The current study replicated previous results that successful transfer was hard to observe in the laboratory settings without explicit prompts. The memory test and transfer task in this study were too difficult and resulted into to the poor performance of the participants. The initial hypothesis had been neither supported nor rejected. To test the hypothesis, future studies could reduce the time interval between study and test, and modified the transfer task to lower the difficulty of the experiment. / text

published_or_final_version / abstract / toc / Electrical and Electronic Engineering / Master / Master of Philosophy

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