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    » Structural Topic Model for Latent Topical Structure Analysis
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    BIOCOMP
    2006
    233views Bioinformatics» more  BIOCOMP 2006»
    Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
    13 years 10 months ago
    Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
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    Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectatio...
    Yu Zhang, Zhidong Deng, Hongshan Jiang, Peifa Jia
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    EMNLP
    2010
    134views Natural Language Processing» more  EMNLP 2010»
    Incorporating Content Structure into Text Analysis Applications
    13 years 7 months ago
    Incorporating Content Structure into Text Analysis Applications
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    In this paper, we investigate how modeling content structure can benefit text analysis applications such as extractive summarization and sentiment analysis. This follows the lingu...
    Christina Sauper, Aria Haghighi, Regina Barzilay
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    EMNLP
    2010
    153views Natural Language Processing» more  EMNLP 2010»
    Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid
    13 years 7 months ago
    Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid
    Download  www.aclweb.org
    Discovering and summarizing opinions from online reviews is an important and challenging task. A commonly-adopted framework generates structured review summaries with aspects and ...
    Wayne Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming ...
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    PAMI
    2008
    190views more  PAMI 2008»
    Scene Classification Using a Hybrid Generative/Discriminative Approach
    13 years 9 months ago
    Scene Classification Using a Hybrid Generative/Discriminative Approach
    Download  cvcl.mit.edu
    We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail we are given a set ...
    Anna Bosch, Andrew Zisserman, Xavier Muñoz
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    NAACL
    2004
    146views Computational Linguistics» more  NAACL 2004»
    Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
    13 years 10 months ago
    Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization
    Download  acl.ldc.upenn.edu
    We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. W...
    Regina Barzilay, Lillian Lee
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