MetaLDA: A topic model that efficiently incorporates meta information

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    21 Citations (Scopus)


    Besides the text content, documents and their associated words usually come with rich sets of meta information, such as categories of documents and semantic/syntactic features of words, like those encoded in word embeddings. Incorporating such meta information directly into the generative process of topic models can improve modelling accuracy and topic quality, especially in the case where the word-occurrence information in the training data is insufficient. In this paper, we present a topic model, called MetaLDA, which is able to leverage either document or word meta information, or both of them jointly. With two data argumentation techniques, we can derive an efficient Gibbs sampling algorithm, which benefits from the fully local conjugacy of the model. Moreover, the algorithm is favoured by the sparsity of the meta information. Extensive experiments on several real world datasets demonstrate that our model achieves comparable or improved performance in terms of both perplexity and topic quality, particularly in handling sparse texts. In addition, compared with other models using meta information, our model runs significantly faster.

    Original languageEnglish
    Title of host publicationProceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
    EditorsVijay Raghavan, Srinivas Aluru, George Karypis, Lucio Miele, Xindong Wu
    Place of PublicationPiscataway USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages10
    ISBN (Print)9781538638347
    Publication statusPublished - 15 Dec 2017
    EventIEEE International Conference on Data Mining 2017 - New Orleans, United States of America
    Duration: 18 Nov 201721 Nov 2017
    Conference number: 17th (Proceedings)


    ConferenceIEEE International Conference on Data Mining 2017
    Abbreviated titleICDM 2017
    Country/TerritoryUnited States of America
    CityNew Orleans
    Internet address


    • Meta information
    • Short texts
    • Topic models

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