A computer-implemented training system is described herein for generating at least one model component based on labeled training data. The training system produces the labels in the training data by leveraging textual information expressed in already-evaluated documents. In another implementation, the training system generates a first model component and a second model component. In one implementation, in an application phase, a computer-implemented model-application system applies the first model component to identify an initial set of related items that are related to an input item (such as a query). The model-application system then applies the second model component to select a subset of related items from among the initial set of related items.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for generating and applying at least one model component, comprising: in a training system that includes one or more computing devices: providing at least one seed item; identifying, for each seed item, a set of candidate items; using a computer-implemented label-generating component to generate a label for each pairing of a particular seed item and a particular candidate item, to collectively provide label information, the label being generated, using the label-generating component, by: identifying a set of documents that have established respective evaluation measures, each evaluation measure reflecting an assessed relevance between a particular document in the set of documents and the particular seed item; determining whether the particular candidate item is found in each document in the set of documents, to provide retrieval information; and generating the label for the particular candidate item based on the evaluation measures associated with the documents in the set of documents and the retrieval information; using a computer-implemented feature-generating component to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item, to collectively provide feature information; using a computer-implemented model-generating component to generate and store a model component based on the label information and the feature information; and in a model-application system that includes one or more computing devices: receiving an input item; applying the model component to generate a set of zero, one, or more related items that are determined, by the model component, to be related to the input item; generating an output result based at least on the set of related items; and providing the output result to an end user, the model-application system leveraging use of the model component to facilitate efficient generation of the output result. 2. The method of claim 1, wherein said identifying of the set of candidate items, as applied with respect to the particular seed item, comprises identifying one or more items that have a nexus to the particular seed item, as assessed based on one or more data sources. 3. The method of claim 1, wherein each document, in the set of documents, is associated with a collection of text items, and wherein the collection of text items encompasses text items within the document as well as text items that are determined to relate to the document. 4. The method of claim 1, wherein said generating of the label for the particular candidate item comprises: generating a retrieved gain measure, corresponding to an aggregation of evaluation measures associated with a subset of documents, among the set of documents, that match the particular candidate item; generating a total gain available measure, corresponding to an aggregation of evaluation measures associated with all of the documents in the set of documents; generating a documents-retrieved measure, which corresponds to a number of documents, among the set of documents, that match the particular candidate item; and generating the label based on the retrieved gain measure, the total gain available measure, and the documents-retrieved measure. 5. The method of claim 4, wherein the label is generated by multiplying the total gain available measure by the documents-retrieved measure, to form a product, and dividing the retrieved gain measure by the product. 6. The method of claim 4, wherein at least one of the retrieved gain measure, the total gain available measure, and/or the documents-retrieved measure is modified by an exponential balancing parameter. 7. The method of claim 1, wherein said generating of the set of feature values, for the pairing of the particular seed item and the particular candidate item, comprises determining at least one feature value that assesses a text-based similarity between the particular seed item and the particular candidate item. 8. The method of claim 1, wherein said generating of the set of feature values, for the pairing of the particular seed item and the particular candidate item, comprises determining at least one feature value by applying a language model component to determine a probability of an occurrence of the particular candidate item within a language. 9. The method of claim 1, wherein said generating of the particular set of feature values, for the pairing of the particular seed item and the particular candidate item, comprises determining at least one feature value by applying a translation model component to determine a probability that the particular seed item is transformable into the particular candidate item, or vice versa. 10. The method of claim 1, wherein said generating of the particular set of feature values, for the pairing of the particular seed item and the particular candidate item, comprises determining at least one feature value by determining characteristics of prior user behavior pertaining to the particular seed item and/or the particular candidate item. 11. The method of claim 1, wherein the model component that is generated corresponds to a first model component, and wherein the method further comprises: using the training system to generate a second model component; using the model-application system to apply the first model component to generate an initial set of related items that are related to the input item; and using the model-application system to apply the second model component to select a subset of related items from among the initial set of related items. 12. The method of claim 11, wherein the said training system generates the second model component by: using the first model component to generate a plurality of new individual candidate items; generating a plurality of group candidate items, each of which reflects a particular combination of one or more new individual candidate items; using another computer-implemented label-generating component to generate new label information for the group candidate items; using another computer-implemented feature-generating component to generate new feature information for the group candidate items; and using another computer-implemented model-generating component to generate the second model component based on the new label information and the new feature information. 13. The method of claim 1, wherein each of the set of candidate items corresponds to a group candidate item that includes a combination of individual candidate items, selected from among a set of possible combinations, the individual candidate items being generated using any type of candidate-generating component. 14. The method of claim 13, wherein said using of the feature-generating component to generate feature information comprises, for each particular group candidate item: determining a set of feature values for each individual candidate item that is associated with the particular group candidate item, to overall provide a collection of feature sets that is associated with the particular group candidate item; and determining at least one feature value that provides group-based information that summarizes the collection of feature sets. 15. The method of claim 1, wherein: the model-application system implements a search service, the input item corresponds to an input query, and the set of related items corresponds to a set of linguistic items that are determined to be related to the input query. 16. A computer readable storage medium for storing computer readable instructions, the computer readable instructions implementing a training system when executed by one or more processing devices, the computer readable instructions comprising: logic configured to identify, for each of a set of seed items, a set of candidate items; logic configured to generate a label, for each pairing of a particular seed item and a particular candidate item, based on: evaluation measures which measure an extent to which documents in a set of documents have been assessed as being relevant to the particular seed item; and retrieval information which reflects an extent to which the particular candidate item is found in the set of documents; logic configured to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item, said logic configured to generate a label collectively providing label information, when applied to all pairings of seed items and candidate items, said logic configured to generate a set of feature values collectively providing feature information, when applied to all pairings of seed items and candidate items; and logic configured to generate a model component based on the label information and the feature information, the model component, when applied by a model-application system, identifying, zero, one, or more related items with respect to an input item, each particular candidate item corresponding to a particular individual candidate item that includes a single linguistic item, or a particular group candidate item that includes a combination of individual candidate items. 17. The computer readable storage medium of claim 16, wherein said logic configured to generate the label for the particular candidate item comprises: logic configured to generate a retrieved gain measure, corresponding to an aggregation of evaluation measures associated with a subset of documents, among the set of documents, that match the particular candidate item; and logic configured to generate a total gain available measure, corresponding to an aggregation of evaluation measures associated with all of the documents in the set of documents; logic configured to generate a documents-retrieved measure, which corresponds to a number of documents, among the set of documents, that match the particular candidate item; and logic configured to generate the label based at least on the retrieved gain measure, the total gain available measure, and the documents-retrieved measure. 18. One or more computing devices for implementing at least a training system, comprising: a candidate-generating component configured to generate a set of candidate items for each seed item, for a plurality of seed items; a label-generating component configured to generate a label for each pairing of a particular seed item and a particular candidate item, to collectively provide label information, said label being generated, using the label-generating component, by: identifying a set of documents that have established respective evaluation measures, each evaluation measure reflecting an assessed relevance between a particular document in the set of documents and the particular seed item; determining whether the particular candidate item is found in each document in the set of documents, to provide retrieval information; and generating the label for the particular candidate item based on the evaluation measures associated with the documents in the set of documents and the retrieval information; a feature-generating component configured to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item, to collectively provide feature information; and a model-training component configured to generate and store a model component based on the label information and the feature information. 19. The one or more computing devices of claims 18, further comprising a model-application system, implemented by the one or more computing devices, and comprising: a user interface component configured to receive an input item from an end user; an item-expansion component configured to apply the model component to generate a set of zero, one, or more related items that are determined, by the model component, to be related to the input item; and a processing component configured to generate an output result based on the set of related items, the user interface component further being configured to provide the output result to the end user. 20. The one or more computing devices of claim 19, wherein: the model component that is generated by the training system corresponds to a first model component, the training system is further configured to generate a second model component, the item-expansion component, of the model-application system, is further configured to: apply the first model component to generate an initial set of related items that are related to the input item; and apply the second model component to select a subset of related items from among the initial set of related items.