Weak Supervision for Semi-Supervised Topic Modeling via Word Embeddings
|Title:||Weak Supervision for Semi-Supervised Topic Modeling via Word Embeddings||Authors:||Conheady, Gerald
|Permanent link:||http://hdl.handle.net/10197/8691||Date:||20-Jun-2017||Abstract:||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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Keywords:||Machine learning; Statistics||Other versions:||http://ldk2017.org/||Language:||en||Status of Item:||Peer reviewed||Conference Details:||LDK 2017: Language, Data and Knowledge, Galway Ireland, 19-20 June 2017|
|Appears in Collections:||Computer Science Research Collection|
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