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|Title:||An error driven approach to query segmentation||Authors:||Zhang, W.
Search log mining
|Issue Date:||2013||Citation:||Zhang, W.,Cao, Y.,Lin, C.-Y.,Su, J.,Tan, C.-L. (2013). An error driven approach to query segmentation. WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web : 127-128. ScholarBank@NUS Repository.||Abstract:||Query segmentation is the task of splitting a query into a sequence of non-overlapping segments that completely cover all tokens in the query. The majority of query segmentation methods are unsupervised. In this paper, we propose an error-driven approach to query segmentation (EDQS) with the help of search logs, which enables unsupervised training with guidance from the system-specific errors. In EDQS, we first detect the system's errors by examining the consistency among the segmentations of similar queries. Then, a model is trained by the detected errors to select the correct segmentation of a new query from the top-n outputs of the system. Our evaluation results show that EDQS can significantly boost the performance of state-of-the-art query segmentation methods on a publicly available data set.||Source Title:||WWW 2013 Companion - Proceedings of the 22nd International Conference on World Wide Web||URI:||http://scholarbank.nus.edu.sg/handle/10635/78010||ISBN:||9781450320382|
|Appears in Collections:||Staff Publications|
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