Weak Supervision for Semi-Supervised Topic Modeling via Word Embeddings

LDK 2017: Language, Data and Knowledge, Galway Ireland, 19-20 June 2017Semi-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.Science Foundation Irelan