Servicenow, Inc.

Dariush Shahgoshtasbi

James Crotinger

Brian Robert Silverstein

Qingbin Li

Omer Anil Turkkan

Sagar Davasam Suryanarayan

Jeevan Anand Anne

Darren Hou

Yujie Fang

An embodiment may involve: obtaining textual content including a plurality of token strings, wherein each of the plurality of token strings includes one or more tokens; determining, for the plurality of token strings, respectively corresponding sets of n-gram tuples; assigning respective weights to the plurality of token strings, wherein, for each of the plurality of token strings, the assignment is based on the respectively corresponding set of n-gram tuples; identifying a subset of the plurality of token strings, wherein each of the subset of the plurality of token strings is characterized by a respective weight that exceeds a predetermined threshold weight; and storing sets of n-gram tuples respectively corresponding to the subset of the plurality of token strings.

Language model preprocessing with weighted n-grams

Origin text content to be analyzed using natural language processing is received. The received origin text content is preprocessed using one or more processors including by vectorizing at least a portion of the received origin text content and identifying a closest matching centroid to automatically generate a reduced version of the origin text content to assist in satisfying a constraint of a natural language processing model. The reduced version of the origin text content is used as an input to the natural language processing model. A result of the natural language processing model is provided for use in managing a computerized workflow.

Labeled clustering preprocessing for natural language processing

Origin text content to be analyzed using natural language processing is received. A two-dimensional item sequence representation for at least a portion of the received origin text content is generated. Using one or more processors, one or more evaluation metrics are determined based on an analysis of the two-dimensional item sequence representation. A reduced version of the origin text content is automatically generated based on the one or more evaluation metrics to assist in satisfying a constraint of a natural language processing model. The reduced version of the origin text content is used as an input to the natural language processing model.

Multi-dimensional N-gram preprocessing for natural language processing

A computing device is provided that is configured to obtain, from a managed network, a plurality of response times of a network-based service provided by the managed network; to train, based on the plurality of response times, a probability distribution to model the managed network; to receive an additional response time from the managed network; to use the probability distribution to determine, for the additional response time, a percentile based on the additional response time; based on the percentile, to determine that the additional response time is anomalously high with respect to the plurality of response times of the network-based service; and to transmit, to a client device associated with the managed network, an indication that the additional response time is anomalously high. The probability distribution includes a central portion based on a plurality of bins and a tail portion based on a parametric distribution.

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Bellevue, WA, United States of America

Dariush Shahgoshtasbi