Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Extracting structured information from user queries with semi-supervised conditional random fields
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Efficient computation of entropy gradient for semi-supervised conditional random fields
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Unsupervised query segmentation using click data: preliminary results
Proceedings of the 19th international conference on World wide web
Combining coregularization and consensus-based self-training for multilingual text categorization
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Hi-index | 0.00 |
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.