Using Search-Logs to Improve Query Tagging

Kuzman Ganchev, Keith B. Hall, Ryan T. McDonald, Slav Petrov

Abstract: 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-of-the-art approach, we achieve more than 20% relative error reduction. Additionally, we annotate a corpus of search queries with part-of-speech tags, providing a resource for future work on syntactic query analysis.