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Author: search.filters.author.Chien, Jen-TzungSubject: search.filters.subject.Topic model

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    Latent Dirichlet mixture model
    (2018-02-22)
    Chien, Jen-Tzung
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    Lee, Chao-Hsi
    ;
    Tan, Zheng-Hua
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    電機工程學系
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    Department of Electrical and Computer Engineering
    Text representation based on latent topic model is seen as a non-Gaussian problem where the observed words and latent topics are multinomial variables and the topic proportionals are Dirichlet variables. Traditional topic model is established by introducing a single Dirichlet prior to characterize the topic proportionals. The words in a text document are represented by a random mixture of semantic topics. However, in real world, a single Dirichlet distribution may not faithfully reflect the variations of topic proportionals estimated from the heterogeneous documents. To address these variations, we propose a new latent variable model where latent topics and their proportionals are learned by incorporating the prior based on Dirichlet mixture model. The resulting latent Dirichlet mixture model (LDMM) is constructed for topic clustering as well as document clustering. Multiple Dirichlets provide a solution to build structural latent variables in learning representation over a variety of topics. This study carries out the inference for LDMM according to the variational Bayes and the collapsed variational Bayes. Such an unsupervised LDMM is further extended to a supervised LDMM for text classification. Experiments on document representation, summarization and classification show the merit of structural prior in LDMM topic models. (C) 2017 Elsevier B. V. All rights reserved.
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    HIERARCHICAL THEME AND TOPIC MODEL FOR SUMMARIZATION
    (2013-01-01)
    Chien, Jen-Tzung
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    Chang, Ying-Lan
    ;
    電機資訊學士班
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    Undergraduate Honors Program of Electrical Engineering and Computer Science
    This paper presents a hierarchical summarization model to extract representative sentences from a set of documents. In this study, we select the thematic sentences and identify the topical words based on a hierarchical theme and topic model (H2TM). The latent themes and topics are inferred from document collection. A tree stick-breaking process is proposed to draw the theme proportions for representation of sentences. The structural learning is performed without fixing the number of themes and topics. This H2TM is delicate and flexible to represent words and sentences from heterogeneous documents. Thematic sentences are effectively extracted for document summarization. In the experiments, the proposed H2TM outperforms the other methods in terms of precision, recall and F-measure.
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    Bayesian Sparse Topic Model
    (2014-03-01)
    Chien, Jen-Tzung
    ;
    Chang, Ying-Lan
    ;
    電機資訊學士班
    ;
    Undergraduate Honors Program of Electrical Engineering and Computer Science
    This paper presents a new Bayesian sparse learning approach to select salient lexical features for sparse topic modeling. The Bayesian learning based on latent Dirichlet allocation (LDA) is performed by incorporating the spike-and-slab priors. According to this sparse LDA (sLDA), the spike distribution is used to select salient words while the slab distribution is applied to establish the latent topic model based on those selected relevant words. The variational inference procedure is developed to estimate prior parameters for sLDA. In the experiments on document modeling using LDA and sLDA, we find that the proposed sLDA does not only reduce the model perplexity but also reduce the memory and computation costs. Bayesian feature selection method does effectively identify relevant topic words for building sparse topic model.