Systems and methods to facilitate local searches via location disambiguation
First Claim
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1. A method, comprising:
- receiving, at a computing device from a searcher, a search query having location ambiguity;
generating, by the computing device, a location model using a machine learning technique, the location model generated, at least in part, based on a personalization feature, wherein the personalization feature comprises a location corresponding to a past result selected by the searcher, wherein generating the location model based, at least in part, on the personalization feature comprises generating a training dataset comprising the personalization feature, wherein generating the training dataset comprisesfiltering a query log to identify a first query, the first query specifying an unambiguous location,generating, from the first query, a second query having location ambiguity by removing a first component of the first query, wherein removing the first component comprisesbreaking the first query into the first component and a second component, andremoving the first component,identifying a location candidate for the second query, andgenerating a training target based on comparing the unambiguous location specified in the first query and the location candidate for the second query;
identifying, by the computing device using the location model, an unambiguous location for the search query to resolve the location ambiguity; and
determining, by the computing device, a search result corresponding to the search query directed to the unambiguous location for the search query.
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Abstract
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.
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Citations
16 Claims
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1. A method, comprising:
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receiving, at a computing device from a searcher, a search query having location ambiguity; generating, by the computing device, a location model using a machine learning technique, the location model generated, at least in part, based on a personalization feature, wherein the personalization feature comprises a location corresponding to a past result selected by the searcher, wherein generating the location model based, at least in part, on the personalization feature comprises generating a training dataset comprising the personalization feature, wherein generating the training dataset comprises filtering a query log to identify a first query, the first query specifying an unambiguous location, generating, from the first query, a second query having location ambiguity by removing a first component of the first query, wherein removing the first component comprises breaking the first query into the first component and a second component, and removing the first component, identifying a location candidate for the second query, and generating a training target based on comparing the unambiguous location specified in the first query and the location candidate for the second query; identifying, by the computing device using the location model, an unambiguous location for the search query to resolve the location ambiguity; and determining, by the computing device, a search result corresponding to the search query directed to the unambiguous location for the search query. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A non-transitory computer readable medium storing instructions which, when executed by a computer comprising a processor, cause the computer to perform operations comprising:
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receiving a search query having location ambiguity; generating a location model using a machine learning technique, the location model generated, at least in part, based on a personalization feature, wherein the personalization feature comprises a location corresponding to a past result selected by a searcher, wherein generating the location model based, at least in part, on the personalization feature comprises generating a training dataset comprising the personalization feature, wherein generating the training dataset comprises filtering a query log to identify a first query, the first query specifying an unambiguous location, generating, from the first query, a second query having location ambiguity by removing a first component of the first query, wherein removing the first component comprises breaking the first query into the first component and a second component, and removing the first component, identifying a location candidate for the second query, and generating a training target based on comparing the unambiguous location specified in the first query and the location candidate for the second query; identifying, using the location model, an unambiguous location for the search query to resolve the location ambiguity; and determining a search result corresponding to the search query directed to the unambiguous location for the search query. - View Dependent Claims (14)
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15. A system, comprising:
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a processor; and a memory that stores instructions that, when executed by the processor, cause the processor to perform operations comprising receiving a search query having location ambiguity, generating a location model using a machine learning technique, the location model generated, at least in part, based on a personalization feature, wherein the personalization feature comprises a location corresponding to a past result selected by a searcher, wherein generating the location model based, at least in part, on the personalization feature comprises generating a training dataset comprising the personalization feature, wherein generating the training dataset comprises filtering a query log to identify a first query, the first query specifying an unambiguous location, generating, from the first query, a second query having location ambiguity by removing a first component of the first query, wherein removing the first component comprises breaking the first query into the first component and a second component, and removing the first component, identifying a location candidate for the second query, and generating a training target based on comparing the unambiguous location specified in the first query and the location candidate for the second query, identifying, using the location model, an unambiguous location for the search query to resolve the location ambiguity, and determining a search result corresponding to the search query directed to the unambiguous location for the search query. - View Dependent Claims (16)
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Specification