Abstract
Presented herein is a novel model for similar question ranking within collaborative question answer platforms. The presented approach integrates a regression stage to relate topics derived from questions to those derived from question-answer pairs. This helps to avoid problems caused by the differences in vocabulary used within questions and answers, and the tendency for questions to be shorter than answers. The performance of the model is shown to outperform translation methods and topic modelling (without regression) on several real-world datasets.
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Notes
- 1.
Available from http://webscope.sandbox.yahoo.com/catalog.php?datatype=l.
- 2.
Available from https://archive.org/details/stackexchange.
- 3.
Mistakes in the questions are original to the data.
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Chahuara, P., Lampert, T., Gançarski, P. (2016). Retrieving and Ranking Similar Questions from Question-Answer Archives Using Topic Modelling and Topic Distribution Regression. In: Fuhr, N., Kovács, L., Risse, T., Nejdl, W. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2016. Lecture Notes in Computer Science(), vol 9819. Springer, Cham. https://doi.org/10.1007/978-3-319-43997-6_4
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