Yash R . Ghorpade

About

Yash R. Ghorpade is a researcher whose work lies at the intersection of natural language processing and deep learning, with a particular focus on document classification. His most cited paper, "Document Classification using LSTM Neural Network" (2017), has garnered 18 citations and addresses a critical challenge in computer science: the rapid growth of electronic documents and the need for automated categorization. In this work, Ghorpade leverages Long Short-Term Memory (LSTM) networks—a type of recurrent neural network well-suited for sequential data—to improve the accuracy of document classification tasks. By training models on known labels to predict unknown ones, his research contributes to more efficient information retrieval and organization in an era of data overload. This foundational study has influenced subsequent work in text mining and machine learning applications, demonstrating the practical utility of LSTMs for real-world classification problems. Ghorpade’s contributions highlight the importance of deep learning in managing and making sense of unstructured textual data, offering valuable insights for students and researchers exploring automated document analysis and natural language understanding.

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Top Papers

1. 1
   Document Classification using LSTM Neural Network

   18 citations · 2017

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