Semantic Tagging of Web Search Queries - Manshadi, Mehdi; Li, Xiao (ACL, 2009)
We present a novel approach to parse web search queries for the purpose of automatic tagging of the queries. We will define a set of probabilistic context-free rules, which generates bags (i.e. multi-sets) of words. Using this new type of rule in combination with the traditional probabilistic phrase structure rules, we define a hybrid grammar, which treats each search query as a bag of chunks (i.e. phrases). A hybrid probabilistic parser is used to parse the queries. In order to take contextual information into account, a discriminative model is used on top of the parser to re-rank the n-best parse trees generated by the parser. Experiments show that our approach outperforms a basic model, which is based on Conditional Random Fields.