Ensemble Topic Modeling via Matrix Factorization

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Title: Ensemble Topic Modeling via Matrix Factorization
Authors: Belford, Mark
MacNamee, Brian
Greene, Derek
Permanent link: http://hdl.handle.net/10197/8336
Date: 21-Sep-2016
Online since: 2017-02-13T15:06:25Z
Abstract: Topic models can provide us with an insight into the underlying latent structure of a large corpus of documents, facilitating knowledge discovery and information summarization. A range of methods have been proposed in the literature, including probabilistic topic models and techniques based on matrix factorization. However, these methods tend to have stochastic elements in their initialization, which can lead to their output being unstable. That is, if a topic modeling algorithm is applied to the same data multiple times, the output will not necessarily always be the same. With this idea of stability in mind we ask the question – how can we produce a definitive topic model that is both stable and accurate? To address this, we propose a new ensemble topic modeling method, based on Non-negative Matrix Factorization (NMF), which combines a collection of unstable topic models to produce a definitive output. We evaluate this method on an annotated tweet corpus, where we show that this new approach is more accurate and stable than traditional NMF.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: CEUR Workshop Proceedings
Volume: 1751
Copyright (published version): 2016 the Authors
Keywords: Machine learningStatistics
Other versions: http://ceur-ws.org/Vol-1751/
Language: en
Status of Item: Peer reviewed
Is part of: Greene, D., Mac Namee, B. and Ross, R. (eds.). Proceedings of 24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16)
Conference Details: 24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 20-21 September 2016
Appears in Collections:Insight Research Collection

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