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.

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.

Abstract: Implementing query classification includes executing a reductionist module on a query to extract a core term, which term is used to search a hash table that maps core terms to corresponding categories, deriving a first result including one of the categories from the search, and executing an enrichment module on the query to yield a second result. The enrichment module includes searching an index of terms that are mapped to documents and corresponding categories. Upon determining the core term is present in the hash table, a weighted average is calculated for values of the first and second results based on training data. Upon determining the core term from the query is not in the hash table, and also that a probability score of the category in the index for the second result meets a minimum confidence value, the core term and the corresponding categories are stored in the hash table.

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.

Abstract: Implementing query classification includes executing a reductionist module on a query to extract a core term, which term is used to search a hash table that maps core terms to corresponding categories; deriving a first result including one of the categories from the search; and executing an enrichment module on the query to yield a second result. The enrichment module includes searching an index of terms that are mapped to documents and corresponding categories. Upon determining the core term is present in the hash table, a weighted average is calculated for values of the first and second results based on training data. Upon determining the core term from the query is not in the hash table, and also that a probability score of the category in the index for the second result meets a minimum confidence value, the core term and the corresponding categories are stored in the hash table.

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.

Abstract: Implementing query classification includes receiving a core term from a search query responsive to execution of a first module. The first module searches a table for the core term and yields a first result. The query classification also includes receiving a second result from the search query responsive to execution of a second module. The second module searches an index of terms that are mapped to documents and corresponding categories in the index. The second result is indicative of one of the corresponding categories in the index based on a probability score. Upon determining the first result is a category associated with the core term in the table, the query classification also includes calculating a weighted average for the first result and the second result. The calculation yields a third result. The query classification further includes transmitting the third result to a computer device that generated the query.

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.

Abstract: Implementing query classification includes receiving a core term from a search query responsive to execution of a first module. The first module searches a table for the core term and yields a first result. The query classification also includes receiving a second result from the search query responsive to execution of a second module. The second module searches an index of terms that are mapped to documents and corresponding categories in the index. The second result is indicative of one of the corresponding categories in the index based on a probability score. Upon determining the first result is a category associated with the core term in the table, the query classification also includes calculating a weighted average for the first result and the second result. The calculation yields a third result. The query classification further includes transmitting the third result to a computer device that generated the query.

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.

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.

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.