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GENERATING STRUCTURED DATA FOR RICH EXPERIENCES FROM UNSTRUCTURED DATA STREAMS

机译:生成来自非结构化数据流的丰富经验的结构化数据

摘要

Aspects of the present disclosure are directed to providing a rich content experience based on information received from unstructured content. A plurality of information items may be obtained from a plurality of data source, where each information item includes unstructured content. The plurality of information items may be provided to a trained machine learning model, where the model is trained with training data that includes information items and corresponding labeled entities for a plurality of historical events. In examples, a formatted request may be received, where the formatted request is associated with one or more labeled entities associated with the trained machine learning model. The trained machine learning model may identify multiple entities from the unstructured content based on the formatted request associated with the one or more labeled entities. In examples, each identified entity of the multiple identified entities is stored as structured content responsive to the formatted request.
机译:本公开的各方面涉及基于从非结构化内容接收的信息提供丰富的内容体验。可以从多个数据源获得多个信息项,其中每个信息项包括非结构化内容。可以向训练的机器学习模型提供多个信息项,其中模型被训练,其中训练数据包括用于多个历史事件的信息项和相应标记的实体。在示例中,可以接收格式化请求,其中格式化的请求与与培训的机器学习模型相关联的一个或多个标记的实体相关联。训练的机器学习模型可以基于与一个或多个标记实体相关联的格式化请求来识别来自非结构化内容的多个实体。在示例中,多个识别实体的每个识别的实体响应于格式化请求被存储为结构化内容。

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