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Topic modeling analysis of the Allen Human Brain Atlas. [PDF]
Sci RepPizzini L, Valle F, Osella M, Caselle M.
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Public Engagement with Lung Cancer Screening Information: Topic Modeling of Lung Cancer-Related Reddit Posts. [PDF]
Curr OncolJaiswal A +5 more
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Analyzing Reddit Social Media Content in the United States Related to H5N1: Sentiment and Topic Modeling Study. [PDF]
J Med Internet ResPang O +6 more
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Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
David M. Blei, John D. Lafferty
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A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
David M. Blei, John D. Lafferty
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Neurocomputing, 2012
In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms.
Eduardo H. Ramírez +3 more
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In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms.
Eduardo H. Ramírez +3 more
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2015 International Conference on Information Technology (ICIT), 2015
Topic Modeling has been a useful tool for finding abstract topics (which are collections of words) governing a collection of documents. Each document is then expressed as a collection of generated topics. The most basic topic model is Latent Dirichlet Allocation (LDA).
Nishma Laitonjam +3 more
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Topic Modeling has been a useful tool for finding abstract topics (which are collections of words) governing a collection of documents. Each document is then expressed as a collection of generated topics. The most basic topic model is Latent Dirichlet Allocation (LDA).
Nishma Laitonjam +3 more
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Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019
Topic modeling has been widely applied in a variety of industrial applications. Training a high-quality model usually requires massive amount of in-domain data, in order to provide comprehensive co-occurrence information for the model to learn. However, industrial data such as medical or financial records are often proprietary or sensitive, which ...
Di Jiang 0004 +6 more
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Topic modeling has been widely applied in a variety of industrial applications. Training a high-quality model usually requires massive amount of in-domain data, in order to provide comprehensive co-occurrence information for the model to learn. However, industrial data such as medical or financial records are often proprietary or sensitive, which ...
Di Jiang 0004 +6 more
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On collocations and topic models
ACM Transactions on Speech and Language Processing, 2013We investigate the impact of preextracting and tokenizing bigram collocations on topic models. Using extensive experiments on four different corpora, we show that incorporating bigram collocations in the document representation creates more parsimonious models and improves topic coherence.
Jey Han Lau +2 more
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Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries, 2012
Concept taxonomies such as MeSH, the ACM Computing Classification System, and the NY Times Subject Headings are frequently used to help organize data. They typically consist of a set of concept names organized in a hierarchy. However, these names and structure are often not sufficient to fully capture the intended meaning of a taxonomy node, and ...
Anton Bakalov +3 more
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Concept taxonomies such as MeSH, the ACM Computing Classification System, and the NY Times Subject Headings are frequently used to help organize data. They typically consist of a set of concept names organized in a hierarchy. However, these names and structure are often not sufficient to fully capture the intended meaning of a taxonomy node, and ...
Anton Bakalov +3 more
openaire +1 more source