Using Search-Logs to Improve Query Tagging

Syntactic analysis of search queries is important for a variety of information-retrieval tasks; however, the lack of annotated data makes training query analysis models difficult. We propose a simple, efficient procedure in which part-of-speech tags are transferred from retrieval-result snippets to queries at training time. Unlike previous work, our final model does not require any additional resources at run-time. Compared to a state-ofthe-art approach, we achieve more than 20% relative error reduction. Additionally, we annotate a corpus of search queries with partof-speech tags, providing a resource for future work on syntactic query analysis.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Kuzman Ganchev, Keith Hall, Ryan T. McDonald, Slav Petrov
• Keywords: ACL 2012, Computational Linguistics, Information Retrieval Tasks, Speech Tags, Syntactic Query