Open Access   Article

Noise Removal from News Web Sites

N. Narwal1

1 Dept. of Computer Science, Maharaja Surajmal Institute (GGSIP University), New Delhi, India.

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Section:Survey Paper, Product Type: Journal Paper
Volume-5 , Issue-9 , Page no. 237-243, Sep-2017


Online published on Sep 30, 2017

Copyright © N. Narwal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: N. Narwal, “Noise Removal from News Web Sites”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.237-243, 2017.

MLA Style Citation: N. Narwal "Noise Removal from News Web Sites." International Journal of Computer Sciences and Engineering 5.9 (2017): 237-243.

APA Style Citation: N. Narwal, (2017). Noise Removal from News Web Sites. International Journal of Computer Sciences and Engineering, 5(9), 237-243.

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Most of the websites comprises of useful information but along with that they contains non-relevant information mostly related to advertisements, copyright, external links etc. This irrelevant information is considered as noise and if we focus on some of the popular English News web sites i.e., Times of India, Hindustan Times, Indian Express etc. consists of 30-40% of news related information and rest are noise content. In this paper we proposed a novel approach that extracts informative content from news web sites in an unsupervised fashion. Our method utilizes the web page segmentation technique to partition the web page into non overlapping rectangular blocks. In our study we used Artificial Neural Network as a classifier to discriminate the rectangular block using their features as relevant or irrelevant blocks. The main content blocks are filtered from the web page and user is presented with clean news web page. Empirical evaluation of our system shows that ANN classifier gives 96.03% accuracy for web content identification that results in accurately filtering of the web page content.

Key-Words / Index Term

Artificial Neural Network, Web Page Segmentation, Visual Blocks, Cosine Similarity


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