Research Article
An Experimental Study with Tensor Flow for Characteristic mining of Mathematical Formulae from a Document
@ARTICLE{10.4108/eai.10-6-2019.159097, author={K. N. Brahmaji Rao and G. Srinivas and P. V. G. D. Prasad Reddy}, title={An Experimental Study with Tensor Flow for Characteristic mining of Mathematical Formulae from a Document}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={6}, number={21}, publisher={EAI}, journal_a={SIS}, year={2019}, month={6}, keywords={}, doi={10.4108/eai.10-6-2019.159097} }- K. N. Brahmaji Rao
G. Srinivas
P. V. G. D. Prasad Reddy
Year: 2019
An Experimental Study with Tensor Flow for Characteristic mining of Mathematical Formulae from a Document
SIS
EAI
DOI: 10.4108/eai.10-6-2019.159097
Abstract
Through this article a deep learning technique is proposed for the extraction and classification of mathematical keywords from textual documents. Extraction of math keywords from textual data is predominant problem as textual documents contain a culmination of mathematical symbols and literals from natural language such as alphabets and words. Separation of these textual words embedded in the mathematical formulae is a complex task. Our proposed technique solves this critical problem of extracting mathematical keywords from textual documents using techniques such as stemming, tokenization and clustering mathematical keywords based on a training set of mathematical keyword and formulae pairs. The performance of the proposed technique is measured using the metrics such as retrieval time, Sensitivity, Accuracy, FPR, FNR, and FDR are used for appraisal of the proposed technique.
Copyright © 2019 K. N. Brahmaji Rao et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
