Microsoft Technology Licensing, LLC

Nicholas Craswell

Hang Li

Jun Xu

Hugh Evan Williams

Paul Nathan Bennett

Gopi Prashanth Gopal

Sanaz Ahari

Gaurav Sareen

Nitin Agrawal

Milad Shokouhi

Chenyan Xiong

Derrick Leslie Connell

Saurabh Kumar Tiwary

Andrew Peter Oakley

Xia Song

Jay Goyal

Christopher A Meyers

Neil Sharman

Daniel Fernando Campos

Corby Louis Rosset

A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Using a multi-task-trained neural network to guide interaction with a query-processing system via useful suggestions

Electronic documents are retrieved from a database and/or from a network of servers. The documents are topic modeled in accordance with a Regularized Latent Semantic Indexing approach. The Regularized Latent Semantic Indexing approach may allow an equation involving an approximation of a term-document matrix to be solved in parallel by multiple calculating units. The equation may include terms that are regularized via either lnorm and/or via lnorm. The Regularized Latent Semantic Indexing approach may be applied to a set, or a fixed number, of documents such that the set of documents is topic modeled. Alternatively, the Regularized Latent Semantic Indexing approach may be applied to a variable number of documents such that, over time, the variable of number of documents is topic modeled.

Regularized latent semantic indexing for topic modeling

Methods, systems, and media are provided for identifying and clustering queries that are rising in popularity. Resultant clustered queries can be compared to other stored queries using textual and temporal correlations. Fresh indices containing information and results from recently crawled content sources are searched to obtain the most recent query activity. Historical indices are also searched to obtain temporally correlated information and results that match the clustered query stream. A weighted average acceleration of a spike can be calculated to distinguish between a legitimate spike and a non-legitimate spike. Legitimate clusters are combined with other stored clusters and presented as grouped content results to a user output device.

Detecting spiking queries

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Redmond, WA, United States of America

Nicholas Craswell