The problem of automatically extracting the most interesting and relevant keyword phrases in a document has been studied extensively as it is crucial for a number of applications. These applications include contextual advertising, automatic text summarization, and user-centric entity detection systems. All these applications can potentially benefit from a successful solution as it enables computational efficiency (by decreasing the input size), noise reduction, or overall improved user satisfaction.In this paper, we study this problem and focus on improving the overall quality of user-centric entity detection systems. First, we review our concept extraction technique, which relies on search engine query logs. We then define a new feature space to represent the interestingness of concepts, and describe a new approach to estimate their relevancy for a given context. We utilize click through data obtained from a large scale user-centric entity detection system - Contextual Shortcuts - to train a model to rank the extracted concepts, and evaluate the resulting model extensively again based on their click through data. Our results show that the learned model outperforms the baseline model, which employs similar features but whose weights are tuned carefully based on empirical observations, and reduces the error rate from 30.22% to 18.66%.
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