Aletras, N. orcid.org/0000-0003-4285-1965 and Mittal, A. (2017) Labeling topics with images using a neural network. In: Jose, J.M., Hauff, C., Altıngovde, I.S., Song, D., Albakour, D., Watt, S. and Tait, J., (eds.) Advances in Information Retrieval : 39th European Conference on IR Research, ECIR 2017, Aberdeen, UK, April 8-13, 2017, Proceedings. 39th European Conference on IR Research (ECIR 2017), 08-13 Apr 2017, Aberdeen, UK. Lecture Notes in Computer Science (10193). Springer International Publishing , pp. 500-505. ISBN 9783319566078
Abstract
Topics generated by topic models are usually represented by lists of t terms or alternatively using short phrases or images. The current state-of-the-art work on labeling topics using images selects images by re-ranking a small set of candidates for a given topic. In this paper, we present a more generic method that can estimate the degree of association between any arbitrary pair of an unseen topic and image using a deep neural network. Our method achieves better runtime performance O(n) compared to O(n2) for the current state-of-the-art method, and is also significantly more accurate.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 Springer International Publishing. This is an author-produced version of a paper subsequently published in ECIR 2017 Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Topic models; Deep neural networks; Topic representation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Oct 2020 13:02 |
Last Modified: | 15 Oct 2020 10:23 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-56608-5_40 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166700 |