11. The system of claim 9 , wherein to generate said document frequency component score indicating the frequency with which that phrase occurs in the item description information for the subset of items, the program instructions are configured to generate that component score based on a term frequency (TF) of the given phrase over the item description information for the subset of items.
9. A system, comprising: a memory; and one or more processors coupled to the memory, wherein the memory comprises program instructions executable by the one or more processors to: from a group of items each associated with respective item description information, identify a subset of items to evaluate for feature phrase identification, wherein the subset of items are related to one another and include a plurality of items that are less than all of the overall group of items; for each item in the subset, evaluate the respective item description information to identify a respective set of candidate phrases; for each phrase in the sets of candidate phrases, generate multiple component scores comprising: a document frequency component score indicating the frequency with which that phrase occurs in the item description information for the subset of items, an inverse document frequency component score indicating the frequency with which that phrase occurs in a corpus of item description information for the overall group of items, and a brand entropy component score, wherein the brand entropy component score penalizes candidate phrases that only occur in item description information of a specified quantity of different item brands; for each phrase in the sets of candidate phrases, combine the multiple component scores generated for that phrase to generate a respective phrase score for that phrase; based on the phrase scores, select a subset of phrases from the sets of candidate phrases as being feature phrases for the subset of items.