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.

Abstract: A system and method is provided which may comprise parsing an unstructured geographic web-search query into a field-based format, by utilizing conditional random fields, learned by semi-supervised automated learning, to parse structured information from the unstructured geographic web-search query. The system and method may also comprise establishing semi-supervised conditional random fields utilizing one of a rule-based finite state machine model and a statistics-based conditional random field model. Systematic geographic parsing may be used with the one of the rule-based finite state machine model and the statistics-based conditional random field model. Parsing an unstructured local geographical web-based query in local domain may be done by applying a learned model parser to the query, using at least one class-based query log from a form-based query system. The learned model parser may comprise at least one class-level n-gram language model-based feature harvested from a structured query log.

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.

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: A system and method is provided which may comprise parsing an unstructured geographic web-search query into a field-based format, by utilizing conditional random fields, learned by semi-supervised automated learning, to parse structured information from the unstructured geographic web-search query. The system and method may also comprise establishing semi-supervised conditional random fields utilizing one of a rule-based finite state machine model and a statistics-based conditional random field model. Systematic geographic parsing may be used with the one of the rule-based finite state machine model and the statistics-based conditional random field model. Parsing an unstructured local geographical web-based query in local domain may be done by applying a learned model parser to the query, using at least one class-based query log from a form-based query system. The learned model parser may comprise at least one class-level n-gram language model-based feature harvested from a structured query log.

Abstract: A method, system and article of manufacture therefor, are disclosed for automatically generating a query from document content.

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: A computer-readable medium comprises data structure for providing information about levels of similarity between pairs of N documents. The data structure comprises a plurality of entries of similarity values representing levels of similarity for a plurality of pairs of the documents. Each of the similarity values represents a level of similarity of one document of a given pair relative to the other document of the given pair. The similarity value of each entry is greater than a threshold similarity value that is greater than zero. The plurality of similarity-value entries are fewer than N2?N in number if the similarity values are asymmetric with regard to document pairing, and the plurality of similarity-value entries are fewer than N 2 - N 2 in number if the similarity values are symmetric with regard to document pairing. A method and apparatus for generating the data structure are described.

Abstract: A method, system and article of manufacture therefor, are disclosed for automatically generating a query from document content.

Abstract: A method, system and article of manufacture therefor, are disclosed for automatically generating a query from document content.

Abstract: A computer-readable medium comprises data structure for providing information about levels of similarity between pairs of N documents. The data structure comprises a plurality of entries of similarity values representing levels of similarity for a plurality of pairs of the documents. Each of the similarity values represents a level of similarity of one document of a given pair relative to the other document of the given pair. The similarity value of each entry is greater than a threshold similarity value that is greater than zero. The plurality of similarity-value entries are fewer than N2?N in number if the similarity values are asymmetric with regard to document pairing, and the plurality of similarity-value entries are fewer than N 2 - N 2 in number if the similarity values are symmetric with regard to document pairing. A method and apparatus for generating the data structure are described.

Abstract: A computer-readable medium comprises data structure for providing information about levels of similarity between pairs of N documents. The data structure comprises a plurality of entries of similarity values representing levels of similarity for a plurality of pairs of the documents. Each of the similarity values represents a level of similarity of one document of a given pair relative to the other document of the given pair. The similarity value of each entry is greater than a threshold similarity value that is greater than zero. The plurality of similarity-value entries are fewer than N2?N in number if the similarity values are asymmetric with regard to document pairing, and the plurality of similarity-value entries are fewer than N 2 - N 2 in number if the similarity values are symmetric with regard to document pairing. A method and apparatus for generating the data structure are described.

Abstract: A technique for representing an information need and employing one or more filters to select documents that satisfy the represented information need, including a technique of creating filters that involves (a) dividing a set of documents into one or more subsets such that each subset can be used as the source of features for creating a filtering profile or used to set or validate the score threshold for the profile and (b) determining whether multiple profiles are required and how to combine them to create an effective filter. Multiple profiles can be incorporated into an individual filter and the individual filters combined to create an ensemble filter. Ensemble filters can then be further combined to create meta filters.

Abstract: A technique for representing an information need and employing one or more filters to select documents that satisfy the represented information need, including a technique of creating filters that involves (a) dividing a set of documents into one or more subsets such that each subset can be used as the source of features for creating a filtering profile or used to set or validate the score threshold for the profile and (b) determining whether multiple profiles are required and how to combine them to create an effective filter. Multiple profiles can be incorporated into an individual filter and the individual filters combined to create an ensemble filter. Ensemble filters can then be further combined to create meta filters.

Abstract: An information need can be modeled by a binary classifier such as support vector machine (SVM). SVMs can exhibit very conservative precision oriented behavior when modeling information needs. This conservative behavior can be overcome by adjusting the position of the hyperplane, the geometric representation of a SVM. The present invention describes a couple of automatic techniques for adjusting the position of an SVM model based upon a beta-gamma thresholding procedure, cross fold validation and retrofitting. This adjustment technique can also be applied to other types of learning strategies.

Abstract: A personality that identifies enrichment themes of a document is associated with a reading or mobile computing device. In one embodiment, a personality is associated with a document reference reading device and attached to each document reference recorded by the document reference reading device. The personality and each recorded document reference are transmitted to a meta-document server. The meta-document server enriches document content identified by the document reference in accordance with the associated personality. The enriched document content is then made available to the user of the reading device.