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Logs of user queries from a search engine (such as Bing or Google) together with the links clicked provide valuable implicit feedback to improve statistical spoken language understanding (SLU) models. In this work, we propose to enrich the existing classification feature set for domain detection with features computed using the click distribution over a set of clicked URLs from search query click logs (QCLs) of user utterances. Since the form of natural language utterances differs stylisticallydoi:10.1109/asru.2011.6163968 dblp:conf/asru/Hakkani-TurTHCFHIP11 fatcat:7z35sldwxjeodlvhi6x5ybtnz4