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Finding Niche Topics using Semi-Supervised Topic Modeling via Word Embeddings
Author(s)
Date Issued
2017-07-31
Date Available
2019-07-08T08:57:26Z
Abstract
Topic modeling techniques generally focus on the discovery of the predominant thematic structures in text corpora. In contrast, a niche topic is made up of a small number of documents related to a common theme. Such a topic may have so few documents relative to the overall corpus size that it fails to be identified when using standard techniques. This paper proposes a new process, called Niche+, for finding these kinds of niche topics. It assumes interactions with a user who can provide a strictly limited level of supervision, which is subsequently employed in semi-supervised matrix factorization. Furthermore, word embeddings are used to provide additional weakly-labeled data. Experimental results show that documents in niche topics can be successfully identified using Niche+. These results are further supported via a use case that explores a real-world company email database.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Publisher
CEUR-WS.org
Start Page
36
End Page
48
Web versions
Language
English
Status of Item
Peer reviewed
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. CEUR Workshop Proceedings 2086, CEUR-WS.org 2018
Conference Details
AICS 2017: 25th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, 7-8 December 2017
This item is made available under a Creative Commons License
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Owning collection
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501
Acquisition Date
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Downloads
125
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