Abstract
Extracting related or exactly the same data from such a great amount of available data is considered a tedious task. After which relating it to the query and the processing it based on the input query that to varying with the user sounds superficial. But information retrieval systems (IRS), chatbots, and question-answering (QA) systems have developed extensively through the decade. Natural language understanding (NLU) and natural language processing (NLP) are the techniques which made it easy for computers to interpret and process the high-level human query language. Through this paper we suggest a model through comparative analysis of bagOfwords (BOW), Word2Vec, TF-IDF, and BERT for selecting an appropriate IPC section based on the input text provided by the user. The proposed approach is based on syntactical analysis followed by semantic analysis. After performing semantic analysis, we perform feature extraction followed by text classification and categorization based on the resemblance with the dataset of the IPC sections. We worked on different models to test the accuracy and efficiency, for bagOfwords (BOW), Word2Vec, TF-IDF, and BERT.
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Harode, B., Prajapat, S., Bhurre, S. (2022). Text Processor for IPC Prediction. In: Singh, P.K., Singh, Y., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 832. Springer, Singapore. https://doi.org/10.1007/978-981-16-8248-3_9
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DOI: https://doi.org/10.1007/978-981-16-8248-3_9
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