“…After preprocessing the data, using latent Dirichlet allocation, topic modeling techniques enabled the categorization of the data according to the topics arising in the published contents of the startups, making it possible to discover that contents can be grouped into five specific topics: “Fintech and ML,” “IT,” “Business Operations,” “Product/Service R&D,” and “Bank and Funding.” …”
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“…Topics and emotions from the tweets were extracted using Latent Dirichlet Allocation (LDA) method and National Research Council (NRC) Lexicon, respectively. …”
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“…METHODS: We conducted a retrospective analysis of questions submitted from August 24, 2020, to August 24, 2021. We used Latent Dirichlet Allocation topic modeling to identify 25 topics among the submissions, then used thematic analysis to interpret the topics based on their top words and submissions. …”
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“…METHOD: We used Latent Dirichlet Allocation (LDA) topic models, an unsupervised natural language processing (NLP) application, to generate themes about Spanish language tweets categorized by Spanish abortion identity labels: (1) proelección (pro‐choice); (2) derecho a decidir (right to choose); (3) proaborto (pro‐abortion); (4) provida (pro‐life); (5) antiaborto (anti‐abortion); and (6) derecho a vivir (right to life). …”
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“…Topic-wise sentiment analysis using the latent Dirichlet allocation technique is performed as well for topic modeling.…”
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“…To construct the full interactome, we developed a statistical modeling approach called MLCrosstalk (multiple-layer crosstalk) based on latent Dirichlet allocation. MLCrosstalk integrates data from multiple sources, including microbes, human protein-coding genes, miRNAs, and human protein–protein interactions. …”
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“…Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. …”
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“…Method: This machine learning study employed an unsupervised topic modelling design using Latent Dirichlet Allocation. The variables are (1) the distribution of a word across documents and (2) the distribution of a word across topics. …”
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“…We performed topic modeling (‘latent Dirichlet allocation’) to identify high-level topics underlying the text descriptions. …”
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“…Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. …”
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“…The microbial sub-community and functional profiling were performed using Latent Dirichlet Allocation and HUMAnN2. Associations of microbial diversity, sub-community structure, and individual microbial features with ischemic stroke risk were evaluated via conditional logistic regression. …”
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“…Redundancy analysis and Dirichlet Multinomial Mixtures were applied to determine covariates of the virome composition and to condense the gut virota into ‘viral community types’, respectively. …”
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“…The structured and unstructured data were then merged into a tabular dataset after vectorization of the clinical text and a dimensional reduction through Latent Dirichlet Allocation. The study used the free and publicly available Medical Information Mart for Intensive Care (MIMIC) III database, on the open AutoML Library AutoGluon. …”
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“…Samples were partitioned by applying Dirichlet’s multinomial mixture to Bifidobacterium species. …”
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“…We then applied a latent Dirichlet allocation probabilistic topic model to explore themes in an unsupervised manner on the collection of 119 986 posts. …”
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“…We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. …”
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“…RESULTS: With the goal of allowing direct comparisons of different learning principles for models from the family of Markov random fields based on the same a-priori information, we derive a generalization of the commonly-used product-Dirichlet prior. We find that the derived prior behaves like a Gaussian prior close to the maximum and like a Laplace prior in the far tails. …”
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“…A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. …”
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“…However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic. METHOD: A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. …”
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“…Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. …”
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