Can I read An Efficient Framework for Algorithmic Metadata Extraction over Scholarly Documents Using Deep Neural Networks on EtoBox?

An Efficient Framework for Algorithmic Metadata Extraction over Scholarly Documents Using Deep Neural Networks by P. Raghavendra Nayaka; Rajeev Ranjan is a Computer Science article available to read on EtoBox.

What is An Efficient Framework for Algorithmic Metadata Extraction over Scholarly Documents Using Deep Neural Networks about?

The conventional text documents have made it possible to efficiently retrieve large amounts of text data with the development of various search engines. However, these traditional search approaches frequently have lower accuracy in retrieval, particularly when documents have certain characteristics that call for more in-depth semantic extraction. A search engine for algorithms called Algorithm Seer has recently been developed. The normal search engine collects the deep textual metadata and pseudo-codes from research papers. However, such a system is unable to accommodate user searches that attempt to identify algorithm-specific information, such as the data sets on which algorithms operate their effectiveness, runtime complication, etc. A number of improvements to the previously suggested algorithm search engine are given in this study. We provide various ways to identify automatically and extract pseudo-codes and phrases which transmit metadata utilizing various machine learning methods. Around the 89,000 text lines are used for conducting the experiments; we provided new properties to extract algorithmic pseudo-codes. These characteristics include feature groups with a focus on c

Who reads An Efficient Framework for Algorithmic Metadata Extraction over Scholarly Documents Using Deep Neural Networks?

Author

P. Raghavendra Nayaka; Rajeev Ranjan

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)