Abstract: A method includes: determining a first bounding box in textual data; categorizing the first bounding box into a paragraph category among a list of categories including a header category, a section header category, the paragraph category, and a noise category; extracting a candidate noun from the text in the categorized first bounding box, as a candidate paragraph noun; extracting a candidate value from the text in the categorized first bounding box; generating a relationship between the candidate paragraph noun and the extracted candidate value; associating the candidate paragraph noun with a candidate header noun; and generating a key performance indicator using the candidate header noun, the candidate paragraph noun, the candidate value, and the generated relationship.

Abstract: A method includes: determining a first bounding box in textual data; categorizing the first bounding box into a paragraph category among a list of categories including a header category, a section header category, the paragraph category, and a noise category; extracting a candidate noun from the text in the categorized first bounding box, as a candidate paragraph noun; extracting a candidate value from the text in the categorized first bounding box; generating a relationship between the candidate paragraph noun and the extracted candidate value; associating the candidate paragraph noun with a candidate header noun; and generating a key performance indicator using the candidate header noun, the candidate paragraph noun, the candidate value, and the generated relationship.

Abstract: A method includes: determining a first bounding box in textual data; categorizing the first bounding box into a paragraph category among a list of categories including a header category, a section header category, the paragraph category, and a noise category; extracting a candidate noun from the text in the categorized first bounding box, as a candidate paragraph noun; extracting a candidate value from the text in the categorized first bounding box; generating a relationship between the candidate paragraph noun and the extracted candidate value; associating the candidate paragraph noun with a candidate header noun; and generating a key performance indicator using the candidate header noun, the candidate paragraph noun, the candidate value, and the generated relationship.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations by generating semantic table representations for table data objects using a token-wise entity type classification mechanism whose output space is defined by a set of defined entity types characterized by an inter-related entity type taxonomy to generate a representation of a table data object that describes per-token semantic inferences and cross-token semantic inferences performed on the table data object in accordance with subject-matter-domain insights as described by the inter-related entity type taxonomy.