Finding Niche Topics using Semi-Supervised Topic Modeling via Word Embeddings
|Title:||Finding Niche Topics using Semi-Supervised Topic Modeling via Word Embeddings||Authors:||Conheady, Gerald; Greene, Derek||Permanent link:||http://hdl.handle.net/10197/10853||Date:||31-Jul-2017||Online since:||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.||Funding Details:||Science Foundation Ireland||metadata.dc.description.othersponsorship:||Insight Research Centre||Type of material:||Conference Publication||Publisher:||CEUR-WS.org||Start page:||36||End page:||48||Keywords:||Modeling techniques; Niche+; Word embeddings; Text corpus exploration; Topic modeling||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. 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|
|Appears in Collections:||Insight Research Collection|
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