| 摘要 |
A user's search experience may be enhanced by providing additional content based upon an understanding of the user's intent. Query tagging, the assigning of semantic labels to terms within a query, is one technique that may be utilized to determine the context of a user's search query. Accordingly, as provided herein, a query tagging model may be updated using one or more stratified lexicons. A list data structure (e.g., lists of phrases obtained from web pages) and seed distribution data (e.g., pre-labeled probability data) may be used by a graph learning technique to obtain an expanded set of phrases and their respective probabilities of corresponding with particular lexicons (e.g., semantic class lexicons). The expanded set of phrases may be used to group phrases into stratified lexicons. The stratified lexicons may be used as features for updating and/or executing the query tagging model.
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