SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A brief survey of web data extraction tools
ACM SIGMOD Record
Discovering informative content blocks from Web documents
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
DOM-based content extraction of HTML documents
WWW '03 Proceedings of the 12th international conference on World Wide Web
XWRAP: An XML-Enabled Wrapper Construction System for Web Information Sources
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Eliminating noisy information in Web pages for data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting unstructured data from template generated web documents
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Extracting article text from the web with maximum subsequence segmentation
Proceedings of the 18th international conference on World wide web
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Web has emerged as the most important source of information in the world. This has resulted in need for automated software components to analyze web pages and harvest useful information from them. However, in typical web pages the informative content is surrounded by a very high degree of noise in the form of advertisements, navigation bars, links to other content, etc. Often the noisy content is interspersed with the main content leaving no clean boundaries between them. This noisy content makes the problem of information harvesting from web pages much harder. Therefore, it is essential to be able to identify main content of a web page and automatically isolate it from noisy content for any further analysis. Most existing approaches rely on prior knowledge of website specific templates and hand-crafted rules specific to websites for extraction of relevant content. We propose a generic approach that does not require prior knowledge of website templates. While HTML DOM analysis and visual layout analysis approaches have sometimes been used, we believe that for higher accuracy in content extraction, the analyzing software needs to mimic a human user and understand content in natural language similar to the way humans intuitively do in order to eliminate noisy content. In this paper, we describe a combination of HTML DOM analysis and Natural Language Processing (NLP) techniques for automated extractions of main article with associated images from web pages.