Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 41 for “"Topic Models"”.
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Topic models for short text data
Topic models are known to suffer from sparsity when applied to short text data. The problem is caused by a reduced number of observations available for a reliable inference (i.e.: the words in a document). A popular heuristic utilized to overcome this problem is to perform before training some form …
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Visualizations for mental health topic models
… problems in their work flow. We believe topic modeling can provide automatic summaries of conversation text to augment note-taking and transcript-reading. Four simple and familiar visualizations were developed to present the model data: 1) a list of conversation topics, 2) a donut chart …
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Non-parametric bayesian methods for structured topic models
… several new and interesting research challenges. Topic modelling, as a promising statistical technique, has gained significant momentum in recent years in information retrieval, sentiment analysis, images processing, etc. Besides existing topic models, the field of topic modelling still needs to …
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Non-parametric bayesian methods for structured topic models
… several new and interesting research challenges. Topic modelling, as a promising statistical technique, has gained significant momentum in recent years in information retrieval, sentiment analysis, images processing, etc. Besides existing topic models, the field of topic modelling still needs to …
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Terrorism on Twitter: Using topic models to examine topoi and digital rituals following mass violence
… enacted through interfaces, and given topical form and structure through topoi. In particular, I deploy topic modeling as a computational method to identify linguistic patterns of discourse present in Twitter responses to three highly publicized mass violence events: the 2013 Boston …
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Topic Modeling for Heterogeneous Digital Libraries: Tailored Approaches Using Large Language Models
… terminology, making achieving clear and coherent topic representations challenging. Existing topic modeling techniques often struggle with such heterogeneous collections, leaving a gap in providing interpretable and meaningful topic labels. This thesis addresses these challenges through a …
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Digital Critical Editing, Digital Text Analysis, and Charles R. Maturin’s Melmoth the Wanderer
… available in multiple formats for analysis, by topic modelling a corpus of texts contemporary to the edition’s text and making visualisations of those topic models available to the user in the edition’s paratexts as a novel way of contextualising the edition text, and finally by allowing users …
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Efficient algorithms for learning mixture models
… learning problems for a class of probabilistic models called mixture models. Mixture models are usually used to model settings where the observed data consists of different sub-populations, yet we only have access to a limited number of samples of the pooled data. It includes many widely used …
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Inference and robotic path planning over high dimensional categorical observations
… highdimensional categorical data. Statistical models, particularly in streaming and computationally constrained settings, have lagged behind data collection. Recent developments in topic modeling for robotics have highlighted the potential to efficiently extract meaningful relationships from …
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LDA based approach for predicting friendship links in live journal social network
… friendship link prediction problem and study a topic modeling approach to this problem. Topic models are among the most effective approaches to latent topic analysis and mining of text data. In particular, Probabilistic Topic models are based upon the idea that documents can be seen as mixtures …
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Inference and Robotic Path Planning over High Dimensional Categorical Observations
… high dimensional categorical data. Statistical models, particularly in streaming and computationally constrained settings, have lagged behind data collection. Recent developments in topic modeling for robotics have highlighted the potential to efficiently extract meaningful relationships from …
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News Analytics for Global Infectious Disease Surveillance
… such sources using text mining methods such as, topic models, deep learning and dependency parsing can lead to automated generation of the mentioned surveillance tools. Moreover, real-time global availability of these open sources from web-based bio-surveillance systems, such as HealthMap and WHO …
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Bayesian nonparametric learning for complicated text mining
… value. It is commonly accepted that Bayesian models with finite-dimensional probability distributions as building blocks, also known as parametric topic models, are effective tools for text mining. However, one problem in existing parametric topic models is that the hidden topic number needs …
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Heike, Jike, Chuangke : creativity in Chinese technology community
… of Chinese hacker magazines; it explored topics discussed in Jike media, or Chinese geek media, using text mining (a type of data mining) methods including co-occurrence analysis, TF-IDF analysis and topic models (based on LDA); this thesis also includes a field study of Chuangke, seeing …
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Order and chaos : articulating support, housing transformation
… A design methodology, appropriate to the topic, models and records transformation based on individual's interventions.
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Reduced traces and JITing in Church
… it allows one to describe classical Al models in compact ways, providing a language for very rich expression. However, for inference in Bayes nets, Hidden Markov Models, and topic models, the very settings for which probabilistic programming languages like Church were designed for, …
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Clustering based causal topic mining
… timestamped document collection in the form of topics that cause a change in a time-series. We develop a conceptual framework for that can be used to analyze different causal topic mining algorithms. We also propose two novel clustering based algorithms - cCTM-CF and cCTM-CoF to generate causal …
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Graphical Representation of Text Semantics
… frequency mapping of words within the text and topic models that essentially give context to word frequencies and proportionalities, images keep intact the semantic and the context of the words in the text. They provide a deeper understanding and can be better interpreted. Models such as AttnGAN …
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An unsupervised approach to identifying causal relations from relevant scenarios
… into clusters of connected events using advanced topic models. Our hypothesis is that events contributing to one particular scenario tend to be strongly correlated, and thus make good candidates for the causal information identification task. Such relationships are identified by generating …
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A medication extraction framework for electronic health records
… hand-built rules and constrained conditional models. We focus on two concept types (i.e., medications and medical conditions) and the pairwise administered-for relation between these two concepts. For medication extraction, we design a rule-based baseline medNERRgreedy med that identifies …
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