Conheady, Gerald

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  • Publication
    Weak Supervision for Semi-Supervised Topic Modeling via Word Embeddings
    (2017-06-20)
    Conheady, Gerald 
    ;
    Greene, Derek 
    Semi-supervised algorithms have been shown to improve the results of topic modeling when applied to unstructured text corpora. However, sufficient supervision is not always available. This paper proposes a new process, Weak+, suitable for use in semi-supervised topic modeling via matrix factorization, when limited supervision is available. This process uses word embeddings to provide additional weakly-labeled data, which can result in improved topic modeling performance.
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