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Weak Supervision for Semi-Supervised Topic Modeling via Word Embeddings

Publication Type: 
Refereed Original Article
Abstract: 
Semi-supervised algorithms have been shown to improve the results of topic modeling when applied to unstructured text corpora. However, sucient supervision is not always available. This paper pro- poses 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.
Digital Object Identifer (DOI): 
xxx
Publication Status: 
Published
Date Accepted for Publication: 
Monday, 3 April, 2017
Publication Date: 
02/05/2017
Journal: 
Conference: Conference on Language, Data and Knowledge
Issue: 
June 2017
Institution: 
UCD
Open access repository: 
No