Abstract: The disclosed embodiments of computer systems and techniques utilize an ensemble semantics framework to combine knowledge acquisition systems that yield significantly higher quality resources than each system in isolation. Gains in entity extraction are achieved by combining state-of-the-art distributional and pattern-based systems with a large set of features from, for example, a webcrawl, query logs, and wisdom of the crowd sources. This results in improved query interpretation and greater relevancy in providing search results and advertising, for example.

Abstract: Different advantageous embodiments provide for response prediction. A social element is received by a prediction mechanism. A feature set is generated for the social element. A prediction is generated using the feature set and a prediction model.

Abstract: In one embodiment, a web service engine server 104 may predict a successive action by a user based on an entity reference 302. The web service engine server 104 may identify an entity reference 302 in a data transmission caused by a user. The web service engine server 104 may determine from the data transmission a user intention towards the entity reference 302 using an intention model based on a transmission log. The web service engine server 104 may predict a related successive web action option 522 for the entity reference 302 based on the user intention.

Abstract: The subject disclosure is directed towards processing data to obtain associations between queries and entities. Association is modeled using a query-entity click graph, blending general query-click logs with vertical query-click logs. Smoothing techniques address the data sparsity in such graphs, including interpolation using a query synonymy model. The association models may be applied to the task of recommending products to web queries, by annotating queries with products from a large catalog and then mining query-product associations through web search session analysis.

Abstract: The disclosed embodiments of computer systems and techniques utilize an ensemble semantics framework to combine knowledge acquisition systems that yield significantly higher quality resources than each system in isolation. Gains in entity extraction are achieved by combining state-of-the-art distributional and pattern-based systems with a large set of features from, for example, a webcrawl, query logs, and wisdom of the crowd sources. This results in improved query interpretation and greater relevancy in providing search results and advertising, for example.

Abstract: The disclosed embodiments of computer systems and techniques utilize an ensemble semantics framework to combine knowledge acquisition systems that yield significantly higher quality resources than each system in isolation. Gains in entity extraction are achieved by combining state-of-the-art distributional and pattern-based systems with a large set of features from, for example, a webcrawl, query logs, and wisdom of the crowd sources. This results in improved query interpretation and greater relevancy in providing search results and advertising, for example.

Abstract: The disclosed embodiments fulfill searches and determine the validity of a large set of noisy facts and rank the set of facts according to a validity score. Embodiments construct a fact graph by linking together facts that share a common relation structure and entity or instance of an argument. Facts are re-ranked and validated using link analysis processes which propagate weight (validity/authority) through the fact graph. The resulting weights for each fact are potentially combined with other scores (such as from fact extraction algorithms) in order to come up with a final ranking of the facts.

Abstract: The present invention provides a method and system for determining related bid terms. The method and system includes accessing a term database to determine a plurality of term pairs, the term pairs being paired terms bidded together in a term bidding operating environment. In the method and system, for each of the plurality of term pairs, the method and system includes determining similarity values for each of the term pairs. The method and system further includes generating a similarity matrix using the determined similarity values. And, the method and system includes generating an output result based on a co-bidded relationship between at least one of the terms and advertising information.

Abstract: The present invention provides a facility for discovering a set of inference rules, such as “X is author of Y?X wrote Y”, “X solved Y?X found a solution to Y”, and “X caused Y?Y is triggered by X”, by analyzing a corpus of natural language text. The corpus is parsed to identify grammatical relationships between words and to build dependency trees formed of the relationships between the words. Paths linking words in the dependency trees are identified. If two paths tend to link the same sets of words, their meanings are taken to be similar. An inference rule is generated for each pair of similar paths. The output of the inventive system is a set of inference rules and a database in which to store these inference rules. The rules generated by the system are interpretable by machines and used in other applications (e.g. information extraction, information retrieval, and machine translation).

Abstract: The present invention provides a facility for discovering a set of inference rules, such as “X is author of Y≈X wrote Y”, “X solved Y≈X found a solution to Y”, and “X caused Y≈Y is triggered by X”, by analyzing a corpus of natural language text. The corpus is parsed to identify grammatical relationships between words and to build dependency trees formed of the relationships between the words. Paths linking words in the dependency trees are identified. If two paths tend to link the same sets of words, their meanings are taken to be similar. An inference rule is generated for each pair of similar paths. The output of the inventive system is a set of inference rules and a database in which to store these inference rules. The rules generated by the system are interpretable by machines and used in other applications (e.g. information extraction, information retrieval, and machine translation).