1. A computer-implemented method, comprising: from a group of items each associated with respective item description information, identifying 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, performing a parsing operation on the item description information of that item in order to generate a respective set of candidate phrases comprising one or more words; applying one or more filters to the multiple sets of candidate phrases to remove ineligible phrases from consideration; for each phrase in the sets of candidate phrases, generating 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, combining the multiple component scores generated for that phrase to generate a respective phrase score for that phrase; based on the phrase scores, selecting a subset of phrases from the sets of candidate phrases as being feature phrases for the subset of items; and within an item detail page to be provided to one or more users, generating information that indicates at least some of the feature phrases are features of one or more items from the subset of items.