Synthetic Dataset Generation for Online Topic Modeling

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Title: Synthetic Dataset Generation for Online Topic Modeling
Authors: Belford, MarkMacNamee, BrianGreene, Derek
Permanent link: http://hdl.handle.net/10197/10845
Date: 12-Apr-2018
Online since: 2019-07-03T07:45:47Z
Abstract: Online topic modeling allows for the discovery of the underlying latent structure in a real time stream of data. In the evaluation of such approaches it is common that a static value for the number of topics is chosen. However, we would expect the number of topics to vary over time due to changes in the underlying structure of the data, known as concept drift and concept shift. We propose a semi-synthetic dataset generator, which can introduce concept drift and concept shift into existing annotated non-temporal datasets, via user-controlled paramaterization. This allows for the creation of multiple different artificial streams of data, where the “correct” number and composition of the topics is known at each point in time. We demonstrate how these generated datasets can be used as an evaluation strategy for online topic modeling approaches.
Funding Details: Science Foundation Ireland
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Conference Publication
Publisher: CEUR-WS.org
Start page: 63
End page: 75
Copyright (published version): 2017 the Author
Keywords: Machine Learning & StatisticsOnline topic modelingSemi-synthetic dataset generatorParamaterization
Other versions: https://dblp.org/db/conf/aics/aics2017
Language: en
Status of Item: Peer reviewed
Is part of: McAuley, J., McKeever, S. (eds.). Proceedings of the 25th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, December 7 - 8, 2017
Conference Details: AICS 2017: 25th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, 7 - 8 December 2017
Appears in Collections:Insight Research Collection

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