Abstract: The present invention is directed toward a system for database querying using natural language generation, which comprises identifying a first set of entities corresponding to an indexed data set in response to a user query, generating a ranked list of query intents using the first set of entities, wherein each item of the list of query intents represents a second set of entities associated with the user query and iterating over the ranked list of query intents to identify a top ranked intent associated to one of a set of predefined query plans. The predefined query plan associated with the top rank intent is executed using the set of entities corresponding to the top ranked intent, the predefined query plan comprising one or more search actions against the indexed data set. A first set of results is then received and a description is generated and transmitted.

Abstract: The present invention is directed toward a system for database querying using natural language generation, which comprises identifying a first set of entities corresponding to an indexed data set in response to a user query, generating a ranked list of query intents using the first set of entities, wherein each item of the list of query intents represents a second set of entities associated with the user query and iterating over the ranked list of query intents to identify a top ranked intent associated to one of a set of predefined query plans. The predefined query plan associated with the top rank intent is executed using the set of entities corresponding to the top ranked intent, the predefined query plan comprising one or more search actions against the indexed data set. A first set of results is then received and a description is generated and transmitted.

Abstract: Disclosed are methods and apparatus for executing a search query. In accordance with one embodiment, a search query is obtained. The search query is classified into one or more of a plurality of categories. The search query is executed for each of the one or more of the plurality of categories. Search results corresponding to the search query are obtained for each of the one or more of the plurality of categories. The search results are then provided for each of the one or more of the plurality of categories.

Abstract: A system for and method for ranking results. The system includes a server configured to receive a query and an advertisement engine configured to receive the query from the server. The advertisement engine ranks advertisements based on various features, including at least one word overlap feature and a correlation feature.

Abstract: In one embodiment, a search query is received. Information identifying a bookmark representing the search query is automatically stored in association with a set of bookmarks. Search results corresponding to the search query are automatically obtained and provided, where the search results identify one or more documents. When one of the documents is selected, a link to the selected one of the documents is automatically stored in association with the bookmark.

Abstract: Disclosed herein is parallel processing of a query, which uses inter-query parallelism in posting list intersections. A plurality of tasks, e.g., posting list intersection tasks, are identified for processing in parallel by a plurality of processing units, e.g., a plurality of processing cores of a multi-core system.

Abstract: Disclosed are methods and apparatus for automatically storing and generating bookmarks. In one embodiment, a search query is received. Information identifying a bookmark representing the search query is automatically stored in association with a set of bookmarks. Search results corresponding to the search query are automatically obtained and provided, where the search results identify one or more documents. When one of the documents is selected, a link to the selected one of the documents is automatically stored in association with the bookmark.

Abstract: In one embodiment, a search query is received. Information identifying a bookmark representing the search query is automatically stored in association with a set of bookmarks. Search results corresponding to the search query are automatically obtained and provided, where the search results identify one or more documents. When one of the documents is selected, a link to the selected one of the documents is automatically stored in association with the bookmark.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.

Abstract: Techniques for query processing in a multi-site search engine are described. During an indexing phase, each site of a multi-site search engine indexes a set of assigned web resources and each site calculates, for each term in the set of assigned web resources, a site-specific upper bound ranking score on the contribution of the term to the search engine ranking function for a query containing the term. During a propagation phase, all sites exchange their site-specific upper bound ranking scores with each other. In response to a site receiving a query, the site determines the set of locally matching resources and compares the ranking score of a locally matching resource with the site-specific upper bound ranking scores for the terms of the query that were received during the propagation phase and determines whether to communicate the query to other sites.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.

Abstract: Disclosed herein is parallel processing of a query, which uses inter-query parallelism in posting list intersections. A plurality of tasks, e.g., posting list intersection tasks, are identified for processing in parallel by a plurality of processing units, e.g., a plurality of processing cores of a multi-core system.

Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that takes advantage of machine translation technologies. The system of the present invention implements this goal by means of simple and efficient machine translation features that are extracted from the surrounding context to match with the pool of potential advertisements. Machine translation features used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the machine translation features measures for the advertisements and the surrounding context.

Abstract: A method of caching posting lists to a search engine cache calculates the ratios between the frequencies of the query terms in a past query log and the sizes of the posting lists for each term, and uses these ratios to determine which posting lists should be cached by sorting the ratios in decreasing order and storing to the cache those posting lists corresponding to the highest ratio values. Further, a method of finding an optimal allocation between two parts of a search engine cache evaluates a past query stream based on a relationship between various properties of the stream and the total size of the cache, and uses this information to determine the respective sizes of both parts of the cache.

Abstract: A computer implemented system for search engine facility architecting and design. The system estimates the costs of power and networking based on system parameters, such as average CPU utilization, connection time, and bytes transferred over the network. Regional distribution of facilities may be evaluated to take into account the various parameters and optimize the cost and speed of the systems being designed. The parameters used in analyzing and formulating an architecture are independent of a particular indexing or query processing technique.

Abstract: Disclosed are methods and apparatus for executing a search query. In accordance with one embodiment, a search query is obtained. The search query is classified into one or more of a plurality of categories. The search query is executed for each of the one or more of the plurality of categories. Search results corresponding to the search query are obtained for each of the one or more of the plurality of categories. The search results are then provided for each of the one or more of the plurality of categories.

Abstract: Disclosed are methods and apparatus for automatically storing and generating bookmarks. In one embodiment, a search query is received. Information identifying a bookmark representing the search query is automatically stored in association with a set of bookmarks. Search results corresponding to the search query are automatically obtained and provided, where the search results identify one or more documents. When one of the documents is selected, a link to the selected one of the documents is automatically stored in association with the bookmark.

Abstract: A method and system for quantifying the quality of search results from a search engine based on cohesion. The method and system include modeling a set of search engine search results as a cluster and measuring the cohesion of the cluster. In an embodiment, the cohesion of the cluster is the average similarity between the cluster elements to a centroid vector. The centroid vector is the average of the weights of the vectors of the cluster. The similarity between the centroid vector and the cluster's elements is the cosine similarity measure. Each document in the set of search results is represented by a vector where each cell of the vector represents a stemmed word. Each cell has a cell value which is the frequency of the corresponding stemmed word in a document multiplied by a weight that takes into account the location of the stemmed word within the document.