Abstract
Textual problem-solution repositories are available today in various forms, most commonly as problem-solution pairs from community question answering systems. Modern search engines that operate on the web can suggest possible completions in real-time for users as they type in queries. We study the problem of generating intelligent query suggestions for users of customized search systems that enable querying over problem-solution repositories. Due to the small scale and specialized nature of such systems, we often do not have the luxury of depending on query logs for finding query suggestions. We propose a retrieval model for generating query suggestions for search on a set of problem solution pairs. We harness the problem solution partition inherent in such repositories to improve upon traditional query suggestion mechanisms designed for systems that search over general textual corpora. We evaluate our technique over real problem-solution datasets and illustrate that our technique provides large and statistically significant improvements over the state-of-the-art technique in query suggestion.
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P., D., Chakraborti, S., Khemani, D. (2013). Query Suggestions for Textual Problem Solution Repositories. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_48
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DOI: https://doi.org/10.1007/978-3-642-36973-5_48
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