CFP last date
20 December 2024
Reseach Article

Syntatic Feature based Classification Algorithm to Detect Validity of Text

by Manika Gupta, Vineet Khanna
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 1
Year of Publication: 2017
Authors: Manika Gupta, Vineet Khanna
10.5120/ijca2017911900

Manika Gupta, Vineet Khanna . Syntatic Feature based Classification Algorithm to Detect Validity of Text. International Journal of Computer Applications. 163, 1 ( Apr 2017), 1-4. DOI=10.5120/ijca2017911900

@article{ 10.5120/ijca2017911900,
author = { Manika Gupta, Vineet Khanna },
title = { Syntatic Feature based Classification Algorithm to Detect Validity of Text },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 1 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number1/27356-2017911900/ },
doi = { 10.5120/ijca2017911900 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:56.014884+05:30
%A Manika Gupta
%A Vineet Khanna
%T Syntatic Feature based Classification Algorithm to Detect Validity of Text
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 1
%P 1-4
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The complexity of a natural language itself is very challenging as the natural language is not free from ambiguity problem. It is almost impossible to identify that the given text is having sense or not. In today's scenario it becomes even much important to detect that input is given by human or a machine. A valid input with sense is needed everywhere from Social media platforms to Business Intelligence. This Classification algorithm aims to detect whether the given input text is valid, or randomly typed in a keyboard. It returns a percentage value where a lower one means valid text, and a higher value means random text. The approach is based on identifying that the amount of unique chars, amount of vowels of letters, the word/char ratio (in %) are in a usual range. Then it further calculates "deviation score" to compute the accuracy of given input.

References
  1. Ms. Ranju Marwaha, Data Mining Techniques and Applications in Telecommunication Industry,International Journal of advanced research in computer science and software engineering, Volume 4, Issue 9,September 2014
  2. Jijy George ,Sandhya .N., Suja George,” Classification Problem In Text Mining” International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Volume 1 Issue 8 (September 2014)
  3. Ah-Hwee Tan,”Text Mining:The state of the art and the challenges”
  4. Monica Bali, Deipali Gore,” A Survey on Text Classification with Different Types of Classification Methods, International Journal of Innovative Research in Computer and Communication Engineering Vol. 3, Issue 5, May 2015.
  5. Bhumika, Prof Sukhjit Singh Sehra, Prof Anand Nayyar, A Review Paper On Algorithms Used For Text Classification, Internatioal Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 2, Issue 3, March 2013
  6. Pratik Agrawal, Prof. A.J.Agrawal , Implementation of Semantic Analysis Using Domain Ontology, IOSR Journal of Computer Engineering (IOSR-JCE), 8727Volume 11, Issue 3 (May. - Jun. 2013)
  7. Mita K. Dalal, Mukesh A. Zaveri,” Automatic Text Classification: A Technical Review”, International Journal of Computer Applications (0975 – 8887)Volume 28– No.2, August 2011
  8. Vandana Korde, C Namrata Mahender,” Text Classification And Classifiers:A Survey, International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.2, March 2012
  9. Kush Jain, Priya Khatri and Garima Indolia,” Chunked N-Grams for Sentence Validation” 2015 International Conference on Computational Science
  10. Lakshay Arya,” Sentence Validation by Statistical Language Modeling and Semantic Relations, International Journal of Computer Applications Technology and Research,Volume 3– Issue 12, 812 - 814, 2014
  11. D.Y. Sakhare, Dr. Raj Kumar,” Syntactic and Sentence Feature Based Hybrid Approach for Text Summarization, I.J. Information Technology and Computer Science, 2014, 03, 38-46 Published Online February 2014 in MECS
  12. Ian Tenney.” A general-purpose sentence-level nonsense etector”, December 2014
Index Terms

Computer Science
Information Sciences

Keywords

Data mining text mining text classification sentence validation pattern learning