Abstract: The present disclosure is of a method for automated authoring of purposive models from Natural Language (NL) documents. Conventional model extractors to automatically extract models from NL documents are specific to a metamodel, do not consider document structure and are not configurable. Initially, the system receives a plurality of Natural Language (NL) documents, a metamodel, a plurality of pattern trees corresponding to the metamodel, and a configurable domain dictionary. Each of the plurality of pattern trees includes a plurality of pattern elements. Further, a document information is generated from each of the plurality of NL documents using a document information reading technique. Finally, a plurality of purposive models are generated for each of the plurality of NL documents by interpreting a corresponding document information based on the plurality of pattern trees and the metamodel using a pattern interpretation technique.

Abstract: This disclosure relates to the field of project related document analysis. Conventionally, process of retrieving right information related to a stakeholder with relevant project initiation concerns involves manual intervention resulting in more time consumption. The method of the present disclosure describes a system and method for automated extraction and classification of project initiation related information from request for proposal response documents The RFP response document is parsed using document structure based parsing technique to identify questions and answers. The questions from the RFP response document are classified into different classes of interest and important information from the answers are extracted and mapped to an identified class. The method of the present disclosure demonstrates significant improvement in terms of time consumption by reducing volume of information and providing a quick access to class-specific relevant information from the RFP response document.

Abstract: The present disclosure extracts contextual product feature model from requirement specification documents where the conventional methods fail to perform. Initially, the system receives a plurality of requirement specification documents pertaining to a product, a domain dictionary, a plurality of configuration parameters, and a plurality of extraction patterns. A product feature model is generated using a NLP based feature extraction technique. The product feature model includes a plurality of product feature elements comprising a feature area, a major feature and a plurality of features arranged hierarchically and classified into feature types. Further, a plurality of ContextType associations like core, client, geography and market are extracted for each of the plurality of features using a ContextType extraction technique. Finally, the plurality of ContextType associations is updated in the product feature model to obtain a contextual product feature model.

Abstract: The present disclosure provides a holistic model for feature specification and dependency representation for requirement specification documents where the conventional models fail to provide. The present disclosure receives a plurality of requirement specification documents and a related data. A product feature model is generated based on the plurality of requirement specification documents and the related data using a feature model generation technique. The product feature model includes a plurality of product feature elements. The plurality of product feature elements includes a feature area, a major feature and a plurality of features. A specification model is generated further for each of the plurality of features using a specification extraction technique. Post generating the specification model, a plurality of dependency associations are generated for each of a plurality of specification elements of the specification model using a dependency extraction technique.

Abstract: This disclosure relates generally to document analysis, and more particularly to a method and system for Request For Proposal (RFP) response generation. In one embodiment, the system automatically generates at least one search query in response to a RFP received as input, searches in at least one reference solution database using the generated search query, finds matching data, filters the matching data based on RFP parameter specific data, and prepares a response document. The response document is then provided as an output of the system, in response to the RFP collected as input.

Abstract: This disclosure relates generally to document analysis, and more particularly to a method and system for Request For Proposal (RFP) response generation. In one embodiment, the system automatically generates at least one search query in response to a RFP received as input, searches in at least one reference solution database using the generated search query, finds matching data, filters the matching data based on RFP parameter specific data, and prepares a response document. The response document is then provided as an output of the system, in response to the RFP collected as input.