Thesis (Ph. D.)--RAND Graduate School, 2002. / Includes bibliographical references (p. 96-101).

Thesis (Ph.D. in Nursing) -- University of Colorado Denver, 2008. / Typescript. Includes bibliographical references (leaves 57-65). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;

Thesis (Ph.D.)--RAND Graduate School, 2002. / Includes bibliographical references (p. 96-101).

Originally published as author's thesis, University of Texas at Austin, 1974. / Bibliography: p. 206-211.

Sony’s Support Application team wanted an experiment to be conducted by which they could determine if it was suitable to use Machine Learning to improve the quantity and quality of search results of the in-application search tool. By improving the quantity and quality of the results the team wanted to improve the customer’s journey. A supervised machine learning model was created to classify articles into four categories; Wi-Fi & Connectivity, Apps & Settings, System & Performance, andBattery Power & Charging. The same model was used to create a service that categorized the search terms into one of the four categories. The classified articles and the classified search terms were used to complement the existing search tool. The baseline for the experiment was the result of the search tool without classification. The results of the experiment show that the number of articles did indeed increase but due mainly to the broadness of the categories the search results held low quality.

Trend prediction has become an extremely popular practice in many industrial sectors and academia. It is beneficial for strategic planning and decision making, and facilitates exploring new research directions that are not yet matured. To anticipate future trends in academic environment, a researcher needs to analyze an extensive amount of literature and scientific publications, and gain expertise in the particular research domain. This approach is time-consuming and extremely complicated due to abundance of data and its diversity. Modern machine learning tools, on the other hand, are capable of processing tremendous volumes of data, reaching the real-time human-level performance for various applications. Achieving high performance in unsupervised prediction of emerging trends in text can indicate promising directions for future research and potentially lead to breakthrough discoveries in any field of science. This thesis addresses the problem of emerging trend prediction in text in two main steps: it utilizes HDP topic model to represent latent topic space of a given temporal collection of documents, DBSCAN clustering algorithm to detect groups with high-density regions in the document space potentially leading to emerging trends, and applies KLdivergence in order to capture deviating text which might indicate birth of a new not-yet-seen phenomenon. In order to empirically evaluate the effectiveness of the proposed framework and estimate its predictive capability, both synthetically generated corpora and real-world text collections from arXiv.org, an open-access electronic archive of scientific publications (category: Computer Science), and NIPS publications are used. For synthetic data, a text generator is designed which provides ground truth to evaluate the performance of anomaly detection algorithms. This work contributes to the body of knowledge in the area of emerging trend prediction in several ways. First of all, the method of incorporating topic modeling and anomaly detection algorithms for emerging trend prediction is a novel approach and highlights new perspectives in the subject area. Secondly, the three-level word-document-topic topology of anomalies is formalized in order to detect anomalies in temporal text collections which might lead to emerging trends. Finally, a framework for unsupervised detection of early signals of emerging trends in text is designed. The framework captures new vocabulary, documents with deviating word/topic distribution, and drifts in latent topic space as three main indicators of a novel phenomenon to occur, in accordance with the three-level topology of anomalies. The framework is not limited by particular sources of data and can be applied to any temporal text collections in combination with any online methods for soft clustering.

Text visualization is a field dedicated to the visual representation of textual data by using computer technology. A large number of visualization techniques are available, and now it is becoming harder for researchers and practitioners to choose an optimal technique for a particular task among the existing techniques. To overcome this problem, the ISOVIS Group developed an interactive survey browser for text visualization techniques. ISOVIS researchers gathered papers which describe text visualization techniques or tools and categorized them according to a taxonomy. Several categories were manually assigned to each visualization technique. In this thesis, we aim to analyze the dataset of this browser. We carried out several analyses to find temporal trends and correlations of the categories present in the browser dataset. In addition, a comparison of these categories with a computational approach has been made. Our results show that some categories became more popular than before whereas others have declined in popularity. The cases of positive and negative correlation between various categories have been found and analyzed. Comparison between manually labeled datasets and results of computational text analyses were presented to the experts with an opportunity to refine the dataset. Data which is analyzed in this thesis project is specific to text visualization field, however, methods that are used in the analyses can be generalized for applications to other datasets of scientific literature surveys or, more generally, other manually curated collections of textual documents.

Deep generative models are essential to Natural Language Processing (NLP) due to their outstanding ability to use unlabelled data, to incorporate abundant linguistic features, and to learn interpretable dependencies among data. As the structure becomes deeper and more complex, having an effective and efficient inference method becomes increasingly important. In this thesis, neural variational inference is applied to carry out inference for deep generative models. While traditional variational methods derive an analytic approximation for the intractable distributions over latent variables, here we construct an inference network conditioned on the discrete text input to provide the variational distribution. The powerful neural networks are able to approximate complicated non-linear distributions and grant the possibilities for more interesting and complicated generative models. Therefore, we develop the potential of neural variational inference and apply it to a variety of models for NLP with continuous or discrete latent variables. This thesis is divided into three parts. Part I introduces a <b>generic variational inference framework</b> for generative and conditional models of text. For continuous or discrete latent variables, we apply a continuous reparameterisation trick or the REINFORCE algorithm to build low-variance gradient estimators. To further explore Bayesian non-parametrics in deep neural networks, we propose a family of neural networks that parameterise categorical distributions with continuous latent variables. Using the stick-breaking construction, an unbounded categorical distribution is incorporated into our deep generative models which can be optimised by stochastic gradient back-propagation with a continuous reparameterisation. Part II explores <b>continuous latent variable models for NLP</b>. Chapter 3 discusses the Neural Variational Document Model (NVDM): an unsupervised generative model of text which aims to extract a continuous semantic latent variable for each document. In Chapter 4, the neural topic models modify the neural document models by parameterising categorical distributions with continuous latent variables, where the topics are explicitly modelled by discrete latent variables. The models are further extended to neural unbounded topic models with the help of stick-breaking construction, and a truncation-free variational inference method is proposed based on a Recurrent Stick-breaking construction (RSB). Chapter 5 describes the Neural Answer Selection Model (NASM) for learning a latent stochastic attention mechanism to model the semantics of question-answer pairs and predict their relatedness. Part III discusses <b>discrete latent variable models</b>. Chapter 6 introduces latent sentence compression models. The Auto-encoding Sentence Compression Model (ASC), as a discrete variational auto-encoder, generates a sentence by a sequence of discrete latent variables representing explicit words. The Forced Attention Sentence Compression Model (FSC) incorporates a combined pointer network biased towards the usage of words from source sentence, which significantly improves the performance when jointly trained with the ASC model in a semi-supervised learning fashion. Chapter 7 describes the Latent Intention Dialogue Models (LIDM) that employ a discrete latent variable to learn underlying dialogue intentions. Additionally, the latent intentions can be interpreted as actions guiding the generation of machine responses, which could be further refined autonomously by reinforcement learning. Finally, Chapter 8 summarizes our findings and directions for future work.

This thesis examines Beethoven’s Op. 28 Pastoral Sonata as a musical work that is dominated by the pastoral topic, and, through its use of this topic, refers to certain ideals of the Enlightenment. The first chapter presents an overview of the sonata and its relative neglect by modern musicologists, followed by a brief history of the pastoral topic in music and literature. The second chapter examines, and provides examples of, the pastoral signifiers that occur in the Op. 28 sonata: drone bass, compound meter, subdominant emphasis, simple harmonies, lyrical melodies and the weathered storm. The third chapter summarizes aspects of the Enlightenment that influenced Beethoven, and his use of the pastoral topic to communicate these ideals. The primary arguments put forward are: the Op. 28 Sonata demonstrates aspects of reconciliation between the urban and the rural as a metaphor for the reconciliation between man and God; Beethoven uses dance as symbol of both pastoral and of fraternity in the sonata; and the Enlightenment concept of interconnectedness between all things is reflected in the musical motives and structure of the composition. The thesis concludes by suggesting that the sonata’s message may have been obscured over time because of changes in Beethoven reception history, the gendering of his repertoire, and the shifting perception of what nature signifies as the Romantic Era developed. / Graduate

293f. / Submitted by Suelen Reis (suziy.ellen@gmail.com) on 2013-05-14T17:35:47Z No. of bitstreams: 2 Dissertacao Evanice Lima.pdf: 3922080 bytes, checksum: c7a14dddbd114ecc6a62155e51912b15 (MD5) Tese Edivalda Araújo1.pdf: 1783798 bytes, checksum: f322a381624297e3b950d039e2df989d (MD5) / Approved for entry into archive by Alda Lima da Silva(sivalda@ufba.br) on 2013-06-04T17:28:52Z (GMT) No. of bitstreams: 2 Dissertacao Evanice Lima.pdf: 3922080 bytes, checksum: c7a14dddbd114ecc6a62155e51912b15 (MD5) Tese Edivalda Araújo1.pdf: 1783798 bytes, checksum: f322a381624297e3b950d039e2df989d (MD5) / Made available in DSpace on 2013-06-04T17:28:52Z (GMT). No. of bitstreams: 2 Dissertacao Evanice Lima.pdf: 3922080 bytes, checksum: c7a14dddbd114ecc6a62155e51912b15 (MD5) Tese Edivalda Araújo1.pdf: 1783798 bytes, checksum: f322a381624297e3b950d039e2df989d (MD5) Previous issue date: 2006 / Definimos como objeto de estudo neste trabalho a análise das construções de tópico deslocado à esquerda do português europeu, dos séculos XVIII e XIX, e do português brasileiro, do século XIX, numa perspectiva sintático-discursiva, sob a abordagem da teoria da gramática gerativa e da estrutura da informação, para identificarmos as diferenças e/ou semelhanças sintáticas e discursivas entre essas duas variedades do português a partir da posição sintática ocupada pelo tópico e da sua relação com os outros constituintes da oração, como os advérbios, o sujeito, os elementos interrogativos e os clíticos. Para a realização dessa análise, estabelecemos como corpora cartas pessoais e peças de teatro, retiradas do Corpus do Projeto Tycho Brahe (disponível no site da USP), no caso do português europeu, e coletadas na Biblioteca Pública do Estado da Bahia, no caso do português brasileiro. Levantamos como hipótese a possibilidade de que algumas construções de tópico do português brasileiro poderiam ser explicadas à luz dos dados diacrônicos, principalmente no período selecionado para estudo. A análise dos dados, contudo, revelou que, nesse período, as construções de tópico do português europeu e do português brasileiro ainda não apresentavam diferenças sintáticas que pudessem identificar a primeira como língua de proeminência de sujeito e a segunda como língua de proeminência de tópico. Além disso, não encontramos dados suficientes que pudessem indicar que algumas construções de tópico do português brasileiro atual já estivessem registradas no português europeu. Em termos gerais, entretanto, a partir da comparação entre os dados do português europeu e do português brasileiro, detectamos que: (i) em ambos, é possível a ativação das três posições de tópico no sistema C – TopP1, TopP2 e TopP3 – considerando a proposta de Rizzi (1997, 2002). Essas posições, entretanto, não são ativadas simultaneamente. A escolha do português europeu e do brasileiro é pela seqüência TopP2 + FocP, talvez em função da relação operador-variável entre o foco e a oração. Mas é possível a ocorrência simultânea de TopP1 e TopP2; (ii) não existem, de acordo com os dados, evidências de que tipos diferenciados de tópico ocupem posições diferenciadas na periferia à esquerda; (iii) a identificação da posição do tópico na periferia à esquerda depende da ativação de outras projeções funcionais nessa periferia; (iv) as diferenças observadas entre o português europeu e o português brasileiro não se restringem especificamente às construções de tópico, mas a outros fatores, como: o movimento do verbo auxiliar acima do advérbio baixo, detectado no português europeu, mas com oscilação no português brasileiro; tendência ao preenchimento do sujeito, iniciante no português brasileiro; flutuação na colocação dos clíticos com as construções de tópico, tanto no português europeu quanto no português brasileiro; (v) a posição do tópico tanto no português europeu quanto no português brasileiro sofreu reanálise. O primeiro parece ter desenvolvido construções de tópico mais voltadas para o discurso, daí a sua impossibilidade de colocar IP em segunda posição; enquanto o segundo passou a ter uma direção do tópico mais voltada para a sintaxe, daí a possibilidade de alguns tópicos poderem concordar com o verbo. / Salvador