3. 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; 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, and 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; 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, wherein said selecting comprises applying a rule that specifies a given phrase cannot be selected as a feature phrase for the identified subset of items if that phrase is wholly contained within another phrase that has already been selected as a feature phrase for the identified subset of items.
3. 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; 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, and 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; 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, wherein said selecting comprises applying a rule that specifies a given phrase cannot be selected as a feature phrase for the identified subset of items if that phrase is wholly contained within another phrase that has already been selected as a feature phrase for the identified subset of items. 6. The method of claim 3 , wherein generating said 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 comprises generating that component score based on an inverse document frequency (IDF) of the given phrase over said corpus of item description information.