Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, at a system comprising a processor, a search query submitted by a searcher, the search query having location ambiguity; identifying, by the system, using a location model, an unambiguous location for the search query to resolve the location ambiguity, wherein parameters of the location model used to identify the unambiguous location for the search query comprise at least one of a distance between the searcher and the unambiguous location, a search hit value associated with the unambiguous location, or a number of businesses within the unambiguous location; determining, by the system, a search result corresponding to the unambiguous location identified for the search query; and providing, by the system, the search result as a response to the search query having the location ambiguity. 2. The method of claim 1, further comprising generating the location model using a machine learning technique. 3. The method of claim 2, wherein the machine learning technique comprises a decision tree learning technique. 4. The method of claim 2, wherein the machine learning technique comprises a gradient boosted decision tree learning technique. 5. The method of claim 2, wherein generating the location model comprises generating a first location model for queries received from mobile devices and generating a second location model for queries received from fixed-location devices. 6. The method of claim 2, wherein generating the location model comprises: generating a training dataset; and applying the machine learning technique to the training dataset to generate the location model. 7. The method of claim 6, wherein generating the training dataset comprises: filtering a query log to identify first queries associated with locations without location ambiguity; generating, from the first queries, second queries having location ambiguity; identifying location candidates for the second queries; and computing values of training variables of the training dataset based on comparing the locations without location ambiguity of the first queries and the location candidates for the second queries. 8. A computer readable medium storing instructions which, when executed by a processor of a system, cause the processor to perform operations comprising: receiving a search query submitted by a searcher, the search query having location ambiguity; identifying, using a location model, an unambiguous location for the search query to resolve the location ambiguity, wherein parameters of the location model used to identify the unambiguous location for the search query comprise at least one of a distance between the searcher and the unambiguous location, a search hit value associated with the unambiguous location, or a number of businesses within the unambiguous location; determining a search result corresponding to the unambiguous location identified for the search query; and providing, by the system, the search result as a response to the search query having the location ambiguity. 9. The computer readable medium of claim 8, wherein the operations further comprise generating the location model using a machine learning technique. 10. The computer readable medium of claim 9, wherein the machine learning technique comprises a decision tree learning technique. 11. The computer readable medium of claim 9, wherein the machine learning technique comprises a gradient boosted decision tree learning technique. 12. The computer readable medium of claim 9, wherein generating the location model comprises generating a first location model for queries received from mobile devices and generating a second location model for queries received from fixed-location devices. 13. The computer readable medium of claim 9, wherein generating the location model comprises: generating a training dataset; and applying the machine learning technique to the training dataset to generate the location model. 14. The computer readable medium of claim 13, wherein generating the training dataset comprises: filtering a query log to identify first queries associated with locations without location ambiguity; generating, from the first queries, second queries having location ambiguity; identifying location candidates for the second queries; and computing values of training variables of the training dataset based on comparing the locations without location ambiguity of the first queries and the location candidates for the second queries. 15. A system comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to perform operations comprising receiving a search query submitted by a searcher, the search query having location ambiguity, identifying, using a location model, an unambiguous location for the search query to resolve the location ambiguity, wherein parameters of the location model used to identify the unambiguous location for the search query comprise at least one of a distance between the searcher and the unambiguous location, a search hit value associated with the unambiguous location, or a number of businesses within the unambiguous location, determining a search result corresponding to the unambiguous location identified for the search query, and providing, by the system, the search result as a response to the search query having the location ambiguity. 16. The system of claim 15, wherein the operations further comprise generating the location model using a machine learning technique. 17. The system of claim 16, wherein the machine learning technique comprises a decision tree learning technique. 18. The system of claim 16, wherein generating the location model comprises generating a first location model for queries received from mobile devices and generating a second location model for queries received from fixed-location devices. 19. The system of claim 18, wherein generating the location model comprises: generating a training dataset; and applying the machine learning technique to the training dataset to generate the location model. 20. The system of claim 19, wherein generating the training dataset comprises: filtering a query log to identify first queries associated with locations without location ambiguity; generating, from the first queries, second queries having location ambiguity; identifying location candidates for the second queries; and computing values of training variables of the training dataset based on comparing the locations without location ambiguity of the first queries and the location candidates for the second queries.