Functionality is described herein for analyzing an input linguistic item, such as a query, in a series of stages. The linguistic item includes one or more candidate items. In a first stage, a brand classifier component determines whether the linguistic item specifies at least one brand, to provide a classifier output result. In a second stage, a tagging component generates a set of tags for at least some of the candidate items in the linguistic item, based, in part, on the classifier output result, to generate a tagging output result. An action-taking component then generates at least one result item based on the tagging output result. Functionality is also described herein for producing the brand classifier component and the tagging component using machine-learning training techniques. The training techniques may include provisions to address the later appearance of new brands that do not appear in a brand dictionary.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. One or more computing devices that implement a latency-efficient system for assigning tags to a linguistic item, comprising: a feature generation component configured to generate a plurality of feature values associated with a linguistic item that has been received from a user, a machine-trained brand classifier component configured to determine, based on a first subset of the plurality of feature values, whether the linguistic item specifies at least one brand, to provide a classifier output result; and a machine-trained tagging component configured to determine, based on a second subset of the plurality of feature values, a tag associated with at least one candidate item within the linguistic item, to produce a tagging output result, wherein the first subset of feature values and the second feature values include a shared set of common feature values, and wherein said feature generation component is configured to generate the common feature values once, so as to reduce latency in generating the common feature values. 2. The one or more computing devices of claim 1, further comprising an action-taking component that is configured to generate a result item based, at least in part, on the tagging output result. 3. The one or more computing devices of claim 2, wherein the input linguistic item is a query submitted by the user to a search engine, wherein the tagging output result reflects an intent of the user in submitting the query, and wherein the result item is an item, provided by the search engine to the user in response to the query, that is selected based on the intent of the user. 4. The one or more computing devices of claim 1, wherein the brand classifier component includes a plurality of classifier subcomponents, wherein the tagging component includes a plurality of tagging subcomponents, wherein the plurality of classifier subcomponents and the plurality of tagging subcomponents form a plurality of pairs of classifier subcomponents and tagging subcomponents that are associated with respective product domains, and wherein each classifier subcomponent and tagging subcomponent in a particular pair produces, respectively, a partial classifier result and a partial tagging result, the partial tagging result being based, in part, on the partial classifier result, and wherein the tagging output result encompasses a plurality of partial tagging results produced by the respective pairs of classifier subcomponents and tagging subcomponents. 5. The one or more computing devices of claim 4, further comprising a post-tagging interpretation component that is configured to analyze the plurality of partial tagging results to produce a final output result, corresponding to a partial tagging result that is determined to be most appropriate. 6. A method, implemented by one or more computing devices, for assigning tags to a linguistic item, and performing an action based on the tags, comprising: receiving an input linguistic item from a user that includes one or more candidate items; determining, using a machine-trained brand classifier component, whether the linguistic item specifies at least one brand, to provide a classifier output result; determining, using a machine-trained tagging component, a tag associated with at least one candidate item within the linguistic item, to produce a tagging output result, said determining of the tag being based, in part, on the classifier output result; generating a result item based, at least in part, on the tagging output result; and electronically communicating the result item to the user, said receiving, determining whether the linguistic items specifies at least one brand, determining a tag, generating, and electronically communicating being performed by said one or more computing devices. 7. The method of claim 6, wherein the input linguistic item is a query submitted by the user to a search engine, and wherein the tagging output result reflects an intent of the user in submitting the query. 8. The method of claim 7, wherein the result item is a product page, provided by the search engine, that pertains to a product that is selected based on the intent of the user. 9. The method of claim 7, wherein the result item is a digital advertisement, provided by the search engine, that is selected based on the intent of the user. 10. The method of claim 7, wherein the result item corresponds to a compilation of search result items, provided by the search engine, that is selected based on the intent of the user. 11. The method of claim 6, wherein the brand classifier component includes a plurality of classifier subcomponents, wherein the tagging component includes a plurality of tagging subcomponents, wherein the plurality of classifier subcomponents and the plurality of tagging subcomponents form a plurality of pairs of classifier subcomponents and tagging subcomponents that are associated with respective product domains, and wherein each classifier subcomponent and tagging subcomponent in a particular pair produces, respectively, a partial classifier result and a partial tagging result, the partial tagging result being based, in part, on the partial classifier result, and wherein the tagging output result encompasses a plurality of partial tagging results produced by the respective pairs of classifier subcomponents and tagging subcomponents. 12. The method of claim 11, further comprising analyzing the plurality of partial tagging results in the tagging output result to produce a final output result, corresponding to a partial tagging result that is determined to be most appropriate. 13. The method of claim 6, further comprising generating a plurality of feature values associated with the linguistic item, wherein the brand classifier component operates based on a first subset of the plurality of feature values, and wherein the tagging component operates based on a second subset of the plurality of feature values, wherein the first subset of feature values and the second subset of feature values include a shared set of common feature values, and wherein said generating of the plurality of feature values includes generating the common feature values once, so as to reduce latency in generating the common feature values. 14. The method of claim 6, further comprising, in a machine-learning training process: generating at least one brand classifier model based on a first group of feature values that are generated based on a set of training examples, the brand classifier component being implemented based on said at least one brand classifier model; and generating at least one tagging model based on a second group of feature values that are generated based on the training examples, the tagging component being implemented based on said at least one tagging model, the second group of feature values also including feature values that are generated based on a classifier output result that is produced by the brand classifier component, in response to processing the training examples. 15. The method of claim 14, wherein said generating of said at least one brand classifier model uses a logistic regression machine-learning technique. 16. The method of claim 14, wherein said generating of said at least one tagging model uses a conditional random field machine-learning technique. 17. The method of claim 6, further comprising, in a machine-learning training process: generating a plurality of feature values associated with a set of training examples, wherein at least one feature value, for each particular training example, indicates whether the particular training example specifies a brand that is contained within a brand dictionary; generating at least one brand classifier model based on at least some of the plurality of feature values associated with the training examples, the brand classifier component being implemented based on said at least one brand classifier model; using the brand classifier component to determine whether the training examples specify one or more brands that are not currently contained in the brand dictionary; updating the brand dictionary to include said one or more brands, to provide an updated brand dictionary; and repeating said generating of the plurality of feature values and generating of said at least one brand classifier model based on the updated brand dictionary, to produce at least one updated brand classifier model. 18. The method of claim 17, wherein said generating of the plurality of feature values entails generating the feature values based on only a subset of brands specified in the brand dictionary, the subset of brands being less than a full number of brands in the brand dictionary. 19. A computer readable storage medium for storing computer readable instructions, the computer readable instructions providing an intent determination component when executed by one or more processing devices, the computer readable instructions comprising: logic configured to receive an input linguistic item from a user that includes one or more candidate items; logic configured to determine, using at least one machine-trained preliminary classifier component, whether the linguistic item contains at least one candidate item that matches an identified category of tags, to provide a classifier output result; logic configured to determine, using a machine-trained tagging component, a tag associated with at least one candidate item within the linguistic item, based, at least in part, on the classifier output result, to produce a tagging output result; and logic configured to generate a result item based, at least in part, on the tagging output result. 20. The computer readable storage medium of claim 19, wherein the identified category of tags corresponds to a brand-related category.