Abstract
Thread retrieval is an essential tool in knowledge-based forums. However, forum content quality varies from excellent to mediocre and spam; thus, search methods should find not only relevant threads but also those with high quality content. Some studies have shown that leveraging quality indicators improves thread search. However, these studies ignored the hierarchical and the conversational structures of threads in estimating topical relevance and content quality. In that regard, this paper introduces leveraging post quality indicators in ranking threads. To achieve this, we first use summary statistics measures to convert post level quality features into thread level features. We then train a learning to rank method to combine these thread level features. Preliminary results with some features reveal that representing threads as collections of posts is superior to treating them as concatenations of their posts.
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Albaham, A.T., Salim, N., Adekunle, O.I. (2014). Leveraging Post Level Quality Indicators in Online Forum Thread Retrieval. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_47
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DOI: https://doi.org/10.1007/978-981-4585-18-7_47
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