Automated contextual processing of unstructured data

A method for automated contextual processing, the method comprising:

• classifying, by a processor, using a classification model, a plurality of extracted parameters from a set of digitized training documents, wherein the classification is performed to assign a document similarity score with respect to a set of reference documents corresponding to a plurality of domains;
• detecting automatically, by the processor, a domain for the set of digitized training documents based on the document similarity score;
• loading, by the processor, a domain based neural model for the detected domain to generate a plurality of pre-defined contextual parameters specific to the detected domain, the plurality of pre-defined contextual parameters being obtained by extraction of multiple queries from the set of digitized training documents and subsequent processing of the extracted queries;
• receiving, by the processor, a set of input documents obtained by digitization of a non-digital documents;
• performing, by the processor, using an AI model, a contextual processing of the received set of input documents based on the pre-defined contextual parameters to obtain an output in form of a plurality of filtered snippets, each bearing a corresponding rank, the contextual processing comprising context building, context search and context-based ranking of one or more snippets extracted from the input documents;
• wherein a context-based verification of the unstructured data is performed based on the plurality of filtered snippets and the corresponding rank;
• receiving, by the processor, from a contextual processing engine, a first data comprising the plurality of filtered snippets;
• receiving, by the processor, from a learning engine coupled to the processor, a second data comprising an updated plurality of filtered snippets;
• classifying, by the processor, using one or more models, the first data and the second data in a pre-defined format;
• assigning, by the processor, using the one or more models, a pre-defined weight to each of the classified first data and the classified second data;
• determining, by the processor, using the one or more models, a similarity score for the plurality of filtered snippets based on the assigned weights;
• and
• updating, by the processor, using the one or more models, the rank of each snippet in the plurality of filtered snippets to assign an updated rank.

A non-transitory computer readable medium comprising machine executable instructions that are executable by a processor to:

• classify a plurality of extracted parameters from a set of digitized training documents, wherein the classification is performed so as to assign a document similarity score with respect to a set of reference documents corresponding to a plurality of domains;
• detect automatically, a domain for the set of digitized training documents based on the document similarity score;
• load a domain based neural model for the detected domain to generate a plurality of pre-defined contextual parameters specific to the detected domain, the plurality of pre-defined contextual parameters being obtained by extraction of multiple queries from the set of digitized training documents and subsequent processing of the extracted queries;
• receive a set of input documents that are obtained by digitization of a non-digital input documents through optical character recognition (OCR);
• and
• perform a contextual processing of the received set of input documents based on the pre-defined contextual parameters to obtain an output in form of a plurality of filtered snippets each bearing a corresponding rank, the contextual processing comprising context building, context search and context-based ranking of one or more snippets extracted from the input documents;
• wherein a context-based verification of the unstructured data is performed based on the plurality of filtered snippets and the corresponding rank;
• receive a first data comprising the plurality of filtered snippets;
• receive a second data comprising an updated plurality of filtered snippets;
• classify using one or more models, the first data and the second data in a pre-defined format;
• assign using the one or more models, a pre-defined weight to each of the classified first data and the classified second data;
• determine using the one or more models, a similarity score for the plurality of filtered snippets based on the assigned weights;
• and
• update using the one or more models, the rank of each snippet in the plurality of filtered snippets to assign an updated rank.