16. A non-transitory computer-readable storage medium, storing program instructions computer-executable on a computer to: for each item in a subset of items from a larger group of items, evaluate item description information about that item to identify a respective set of candidate phrases to be evaluated 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 larger group of items; 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, and an inverse document frequency component score indicating the frequency with which that phrase occurs in a corpus of item description information for the larger group of items; 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, wherein the program instructions are configured to generate the respective phrase scores for at least some of the candidate phrases based on a measure of brand entropy for a respective candidate phrase, wherein a given measure of brand entropy is penalized for candidate phrases that have a requisite term frequency—inverse document frequency (TFIDF) but only occur in item description information of a specified quantity of different item brands; and 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.
16. A non-transitory computer-readable storage medium, storing program instructions computer-executable on a computer to: for each item in a subset of items from a larger group of items, evaluate item description information about that item to identify a respective set of candidate phrases to be evaluated 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 larger group of items; 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, and an inverse document frequency component score indicating the frequency with which that phrase occurs in a corpus of item description information for the larger group of items; 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, wherein the program instructions are configured to generate the respective phrase scores for at least some of the candidate phrases based on a measure of brand entropy for a respective candidate phrase, wherein a given measure of brand entropy is penalized for candidate phrases that have a requisite term frequency—inverse document frequency (TFIDF) but only occur in item description information of a specified quantity of different item brands; and 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. 23. The non-transitory medium of claim 16 , wherein the program instructions are configured to generate the respective phrase scores for at least some of the candidate phrases based on query data that indicates the frequency with which the candidate phrases have been used as queries within a search interface.