<link rel="stylesheet" href="styles.cf390c6bfbe7555f.css">

學術出版;;Publications

Permanent URI for this communityhdl:11536/1

收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

Browse

Filters

2013 - 20142

Settings

🏛️國立陽明交通大學機構典藏

Access type: Open accessSubject: search.filters.subject.Topic model

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item type:Item,
    HIERARCHICAL THEME AND TOPIC MODEL FOR SUMMARIZATION
    (2013-01-01)
    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 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.
  • Thumbnail Image
    Item type:Item,
    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.