Abstract: A system and method for training a neural network is implemented for detecting at least one entity in a document to derive relevant inferences therefrom. The method describes obtaining at least one document. The at least one document is processed, via a detection module, to detect a widget entity. The detected widget entity is classified as active or inactive based on a detected state of the widget entity. The classified widget entity is modified into a corresponding machine-readable widget-entity based on the detected state. The at least one document is processed, via an extraction module, to detect a text entity in near vicinity of the classified widget entity. A training pair comprising the machine-readable widget entity and the corresponding text entity is generated. The neural network is trained using the generated training pair.

Abstract: Disclosed is a system and method for training a neural network to be implemented for detecting at least one entity in a document to derive relevant inferences therefrom. The method comprising obtaining at least one document, processing, the at least one document via a detection module to detect a widget entity, wherein the detected widget entity is classified as active or inactive based on a detected state of the widget entity, modifying, the classified widget entity into a corresponding machine-readable widget-entity based on the detected state, processing, the at least one document via an extraction module to detect a text entity in near vicinity of the classified widget entity, generating a training pair comprising the machine-readable widget entity and the corresponding text entity and training the neural network using the generated training pair.