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學術出版;;Publications

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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.

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2013 - 20142

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chang, Ying-LanSubject: search.filters.subject.Bayesian learning

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    Hierarchical Pitman-Yor and Dirichlet Process for Language Model
    (2013-01-01)
    Chien, Jen-Tzung
    ;
    Chang, Ying-Lan
    ;
    電機工程學系
    ;
    Department of Electrical and Computer Engineering
    This paper presents a nonparametric interpretation for modem language model based on the hierarchical Pitman-Yor and Dirichlet (HPYD) process. We propose the HPYD language model (HPYD-LM) which flexibly conducts backoff smoothing and topic clustering through Bayesian nonparametric learning. The nonparametric priors of backoff n-grams and latent topics are tightly coupled in a compound process. A hybrid probability measure is drawn to build the smoothed topic-based LM. The model structure is automatically determined from training data. A new Chinese restaurant scenario is proposed to implement HPYD-LM via Gibbs sampling. This process reflects the power-law property and extracts the semantic topics from natural language. The superiority of HPYD-LM to the related LMs is demonstrated by the experiments on different corpora in terms of perplexity and word error rate.
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    The Nested Indian Buffet Process :for Flexible Topic Modeling
    (2014-01-01)
    Chien, Jen-Tzung
    ;
    Chang, Ying-Lan
    ;
    電機工程學系
    ;
    Department of Electrical and Computer Engineering
    This paper presents a flexible topic model based on the nested Indian buffet process (nIBP). The flexibility is achieved by relaxing three constraints: (1) number of topics is fixed, (2) topics are independent, and (3) topic hierarchy for a document is limited by a single tree path. Bayesian nonparametric learning is conducted to build a tree model where the number of topics and the topic hierarchies are automatically learnt from the given data. In particular, we propose the nIBP to construct the topic mixture model for representation of heterogeneous documents where the mixture components are flexibly selected from tree nodes or dishes that a document or customer chooses in Indian buffet process. The selection is performed in a nested and hierarchical manner. The experiments on document representation show the benefits of using the proposed nIBP.