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Burel, Gregoire and He, Yulan
(2014).
DOI: https://doi.org/10.1145/2567948.2576949
Abstract
We describe the Joint Effort-Topic (JET) model and the Author Joint Effort-Topic (aJET) model that estimate the effort required for users to contribute on different topics. We propose to learn word-level effort taking into account term preference over time and use it to set the priors of our models. Since there is no gold standard which can be easily built, we evaluate them by measuring their abilities to validate expected behaviours such as correlations between user contributions and the associated effort.
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About
- Item ORO ID
- 44017
- Item Type
- Conference or Workshop Item
- ISBN
- 1-4503-2745-1, 978-1-4503-2745-9
- Project Funding Details
-
Funded Project Name Project ID Funding Body Robust 257859 EC-FP7 Not Set GJHZ20120613110641217 Shenzhen International Cooperation Research Funding - Keywords
- natural language processing; text analysis
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Research Group
- Centre for Research in Computing (CRC)
- Copyright Holders
- © 2014 International World Wide Web Conference Committee (IW3C2)
- Related URLs
- Depositing User
- Kay Dave