Weak Supervision for Semi-Supervised Topic Modeling via Word Embeddings

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Title: Weak Supervision for Semi-Supervised Topic Modeling via Word Embeddings
Authors: Conheady, Gerald
Greene, Derek
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 learningStatistics
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
Insight Research Collection

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