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We address the problem of finding multiple groups of words or phrases that explain the underlying query facets, which we refer to as query dimensions. We assume that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists, and query dimensions can be mined out by aggregating these significant lists. Experimental results show that a large number of lists do exist in the top results, and query dimensions generated by grouping these lists are useful for users to learn interesting knowledge about the queries.

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In search engines, different users may search for different information by issuing the same query. To satisfy more users with limited search results, search result diversification re-ranks the results to cover as many user intents as possible. Most existing intent-aware diversification algorithms recognize user intents as subtopics, each of which is usually a word, a phrase, or a piece of description. In this paper, we leverage query facets to understand user intents in diversification, where each facet contains a group of words or phrases that explain an underlying intent of a query. We generate subtopics based on query facets and propose faceted diversification approaches. Experimental results on the public TREC 2009 dataset show that our faceted approaches outperform state-of-the-art diversification models.