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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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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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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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2009 Ninth IEEE International Conference on Data Mining, 2009
In this paper we propose the multirelational topic model (MRTM) for multiple types of link modeling such as citation and coauthor links in document networks. In the citation network, the MRTM models the citation link between each pair of documents as a binary variable conditioned on their topic distributions.
Jia Zeng +3 more
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In this paper we propose the multirelational topic model (MRTM) for multiple types of link modeling such as citation and coauthor links in document networks. In the citation network, the MRTM models the citation link between each pair of documents as a binary variable conditioned on their topic distributions.
Jia Zeng +3 more
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Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002
In this paper, we present a method based on document probes to quantify and diagnose topic structure, distinguishing topics as monolithic, structured, or diffuse. The method also yields a structure analysis that can be used directly to optimize filter (classifier) creation.
David A. Evans 0001 +2 more
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In this paper, we present a method based on document probes to quantify and diagnose topic structure, distinguishing topics as monolithic, structured, or diffuse. The method also yields a structure analysis that can be used directly to optimize filter (classifier) creation.
David A. Evans 0001 +2 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
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Foundations and Trends® in Information Retrieval, 2017
How can a single person understand what’s going on in a collection of millions of documents? This is an increasingly widespread problem: sifting through an organization’s e-mails, understanding a decade worth of newspapers, or characterizing a scientific field’s research. This monograph explores the ways that humans and computers make sense of document
Jordan L. Boyd-Graber +2 more
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How can a single person understand what’s going on in a collection of millions of documents? This is an increasingly widespread problem: sifting through an organization’s e-mails, understanding a decade worth of newspapers, or characterizing a scientific field’s research. This monograph explores the ways that humans and computers make sense of document
Jordan L. Boyd-Graber +2 more
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