Abstract: The present disclosure provides a system (110) for retrofitting words represented using the vectors for Natural Language Processing (NLP) models and a streamlined process which is an ideal pipeline for any NLP tasks. The system (110) may discover the user meta data or k-nuggets in five stages for retrofitting and stacking the retrofitted embeddings. Further, the system (110) may use the retrofitted embeddings for NLP Tasks. The five stages of the k-nugget discovery pipeline are Lexical, Syntactic, Semantic, transactional, and language agnostic stages for retrofitting the word embeddings. The proposed embedding layer is replaced with the retrofitted embedding which may be obtained after the fifth stage and improved performance can be achieved. To validate the approach, the K-nugget discovery pipeline has been tested on the SemEval (Hinglish and English Tweet dataset) and HOT dataset (Hinglish Tweet dataset) and to achieve state of the art results on the test dataset.

Abstract: The present disclosure provides a system and a method for enabling an entity to implement a re-order/consumption rate associated with a product for each vendor/customer and generates a quantity optimization for loss reduction. The product may include perishable consumables utilized by users. The system utilizes clustering of data obtained from customer and merchants. Further, the system implements statistical models to estimate consumption rate of each product, affinity towards products, brands, and popularity of products across locations.

Abstract: The present disclosure provides a system (110) for retrofitting words represented using the vectors for Natural Language Processing (NLP) models and a streamlined process which is an ideal pipeline for any NLP tasks. The system (110) may discover the user meta data or k-nuggets in five stages for retrofitting and stacking the retrofitted embeddings. Further, the system (110) may use the retrofitted embeddings for NLP Tasks. The five stages of the k-nugget discovery pipeline are Lexical, Syntactic, Semantic, transactional, and language agnostic stages for retrofitting the word embeddings. The proposed embedding layer is replaced with the retrofitted embedding which may be obtained after the fifth stage and improved performance can be achieved. To validate the approach, the K-nugget discovery pipeline has been tested on the SemEval (Hinglish and English Tweet dataset) and HOT dataset (Hinglish Tweet dataset) and to achieve state of the art results on the test dataset.

Abstract: The present invention provides a robust and effective solution for a novel pipeline to fine-tune the embedding with very less data and computational resources for making the embeddings semantically and contextually aware. Automated system and method for hyper parameter tuning and retrofitting formulation of information into the embeddings and then propose novel smooth extension to the formulation with a new way to automatically tune hyper parameters pipeline which can be used for wide business problems.