Conduent Business Services, LLC

Sakshi Agarwal

Sandya Srivilliputtur Mannarswamy

Arun Rajkumar

Sharanya Eswaran

Poorvi Agarwal

Methods and systems for forecasting in sparse data streams. In an example embodiment, steps or operations can be implemented for mapping a time series data stream to generate forecast features using a neural network, transforming the forecast features into a space with transformed forecast features thereof using metric learning, clustering the transformed forecast features in a cluster, initializing a forecast learning algorithm with a combination of the transformed forecast features in the cluster corresponding to a sparse data stream, and displaying forecasts in a GUI dashboard with information indicative of how the forecasts were achieved, wherein the mapping, the transforming, the clustering, and the initializing together lead to increases in a speed of the forecasting and computer processing thereof.

Method and system for forecasting in sparse data streams via dense data streams

Additional background information is used with a trained neural network based model to help classify whether text is a subtly and/or ambiguously offensive. This additional background information can come from different sources such as the article on which the comment was made, world knowledge about the external entities (e.g., Wikipedia, Urban Dictionary), phrases referenced in the text being classified, and, the context of the previous comments/text in the thread. The background information is retrieved based on key entities (e.g., people, places things) and/or key phrases in the comment. Sentence matrix encodings are built for both the comment and the background information. The background information encoding is used to condition the comment encoding. The background information encoding, and the conditioned comment encoding are fed to a trained multi-level perceptron to classify the comment as hate speech or non-hate speech.

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West Bengal, India

Sakshi Agarwal