Abstract: Aspects of the present disclosure describe techniques for generating a machine learning model for extracting information from textual content. The method generally includes receiving a training data set including a plurality of documents having related textual strings. A relevancy model is generated from the training data set. The relevancy model is generally configured to generate relevance scores for a plurality of words extracted from the plurality of documents. A knowledge graph model illustrating relationships between the plurality of words extracted from the plurality of documents is generated from the training data set. The relevancy model and the knowledge graph model are aggregated into a complimentary model including a plurality of nodes from the knowledge graph model and weights associated with edges between connected nodes, wherein the weights comprise relevance scores generated from the relevancy model, and the complimentary model is deployed for use in analyzing documents.

Abstract: Aspects of the present disclosure describe techniques for generating a machine learning model for extracting information from textual content. The method generally includes receiving an unstructured document and a structured document including information extracted from the unstructured document and position information associated with the extracted information. The unstructured document is rendered in a first pane, and a graphical rendering of the structured document is rendered in a second pane. The graphical rendering generally may be a structure in which content from the structured document is displayed in a hierarchical format. Each element in the structured document is linked to the rendered unstructured document based on position information included in the structured document.

Abstract: Aspects of the present disclosure describe techniques for generating a machine learning model for extracting information from textual content. The method generally includes receiving a training data set including a plurality of documents having related textual strings. A relevancy model is generated from the training data set. The relevancy model is generally configured to generate relevance scores for a plurality of words extracted from the plurality of documents. A knowledge graph model illustrating relationships between the plurality of words extracted from the plurality of documents is generated from the training data set. The relevancy model and the knowledge graph model are aggregated into a complimentary model including a plurality of nodes from the knowledge graph model and weights associated with edges between connected nodes, wherein the weights comprise relevance scores generated from the relevancy model, and the complimentary model is deployed for use in analyzing documents.

Abstract: Aspects of the present disclosure describe techniques for generating a machine learning model for extracting information from textual content. The method generally includes receiving an unstructured document and a structured document including information extracted from the unstructured document and position information associated with the extracted information. The unstructured document is rendered in a first pane, and a graphical rendering of the structured document is rendered in a second pane. The graphical rendering generally may be a structure in which content from the structured document is displayed in a hierarchical format. Each element in the structured document is linked to the rendered unstructured document based on position information included in the structured document.