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Topic-to-Topic Modeling for COVID-19 Mortality
2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), 2021We examine a cohort of 4307 COVID-19 case fatalities from a de-identified national registry in the U.S. using Latent Dirichlet Allocation and group each patient by topic based on their pre-existing conditions in the years prior to infection and again during the last three weeks of life.
Jeffrey Humpherys +6 more
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Machine Learning, 2013
Topic models have been used extensively as a tool for corpus exploration, and a cottage industry has developed to tweak topic models to better encode human intuitions or to better model data. However, creating such extensions requires expertise in machine learning unavailable to potential end-users of topic modeling software. In this work, we develop a
Yuening Hu +3 more
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Topic models have been used extensively as a tool for corpus exploration, and a cottage industry has developed to tweak topic models to better encode human intuitions or to better model data. However, creating such extensions requires expertise in machine learning unavailable to potential end-users of topic modeling software. In this work, we develop a
Yuening Hu +3 more
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2014 IEEE 4th Workshop on Mining Unstructured Data, 2014
Topic modeling has been applied to several areas of software engineering, such as bug localization, feature location, triaging change requests, and traceability link recovery. Many of these approaches combine mining unstructured data, such as bug reports, with topic modeling a snapshot (or release) of source code.
Christopher S. Corley +3 more
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Topic modeling has been applied to several areas of software engineering, such as bug localization, feature location, triaging change requests, and traceability link recovery. Many of these approaches combine mining unstructured data, such as bug reports, with topic modeling a snapshot (or release) of source code.
Christopher S. Corley +3 more
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2010 IEEE International Conference on Data Mining, 2010
In this paper we propose a framework of topic modeling ensembles, a novel solution to combine the models learned by topic modeling over each partition of the whole corpus. It has the potentials for applications such as distributed topic modeling for large corpora, and incremental topic modeling for rapidly growing corpora.
Zhiyong Shen +3 more
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In this paper we propose a framework of topic modeling ensembles, a novel solution to combine the models learned by topic modeling over each partition of the whole corpus. It has the potentials for applications such as distributed topic modeling for large corpora, and incremental topic modeling for rapidly growing corpora.
Zhiyong Shen +3 more
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Communications of the ACM, 2011
Surveying a suite of algorithms that offer a solution to managing large document archives.
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Surveying a suite of algorithms that offer a solution to managing large document archives.
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Latent topic model for image annotation by modeling topic correlation
2013 IEEE International Conference on Multimedia and Expo (ICME), 2013For the task of image annotation, traditional probabilistic topic models based on Latent Dirichlet Allocation (LDA) [1], assume that an image is a mixture of latent topics. An inevitable limitation of LDA is the inability to model topic correlation since topic proportions of an image are generated independently. Motivated by Correlated Topic Model (CTM)
Xing Xu 0001 +2 more
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Group topic model: organizing topics into groups
Information Retrieval Journal, 2014Latent Dirichlet allocation defines hidden topics to capture latent semantics in text documents. However, it assumes that all the documents are represented by the same topics, resulting in the "forced topic" problem. To solve this problem, we developed a group latent Dirichlet allocation (GLDA).
Ximing Li 0002 +4 more
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2013
Multiplex document networks have multiple types of links such as citation and coauthor links between scientific papers. Inferring thematic topics from multiplex document networks requires quantifying and balancing the influence from different types of links. It is therefore a problem of considerable interest and represents significant challenges.
Juan Yang +2 more
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Multiplex document networks have multiple types of links such as citation and coauthor links between scientific papers. Inferring thematic topics from multiplex document networks requires quantifying and balancing the influence from different types of links. It is therefore a problem of considerable interest and represents significant challenges.
Juan Yang +2 more
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Increasing Topic Coherence by Aggregating Topic Models
2016In this paper, we introduce a novel method for aggregating multiple topic models to produce an aggregate model that contains topics with greater coherence than individual models. When generating a topic model a number of parameters must be specified. Depending on the parameters chosen the resulting topics can be very general or very specific.
Stuart J. Blair +2 more
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Applying a Burst Model to Detect Bursty Topics in a Topic Model
2012This paper focuses on two types of modeling of information flow in news stream, namely, burst analysis and topic modeling. First, when one wants to detect a kind of topics that are paid much more attention than usual, it is usually necessary for him/her to carefully watch every article in news stream at every moment.
Yusuke Takahashi +6 more
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