Most of the information on the Web is inherently structured, product pages of large online shopping sites such as Amazon.com being a typical example. Yet, unstructured keyword queries are still the most common way to search for such structured information, producing an ambiguities and poor ranking, and by that degrading user experience. This problem can be resolved by query segmentation, that is, transformation of unstructured keyword queries into structured queries. The resulting queries can be used to search product databases more accurately, and improve result presentation and query suggestion. The main contribution of our work is a novel approach to query segmentation based on unsupervised machine learning. Its highlight is that query and click-through logs are used for training. Extensive experiments over a large query and click log from a leading shopping engine demonstrate that our approach significantly outperforms baseline.
|Title of host publication||Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM 2011, Glasgow, UK, October 24-28, 2011)|
|Place of Publication||New York NY|
|Publisher||Association for Computing Machinery, Inc|
|Publication status||Published - 2011|