Abstract
In this paper we present a complete solution for automatic cleaning of arbitrary HTML pages with a goal of using web data as a corpus in the area of natural language processing and computational linguistics. We employ a sequence-labeling approach based on Conditional Random Fields (CRF).
Every block of text in analyzed web page is assigned a set of features extracted from the textual content and HTML structure of the page. The blocks are automatically labeled either as content segments containing main web page content, which should be preserved, or as noisy segments not suitable for further linguistic processing, which should be eliminated.
Our solution is based on the tool introduced at the CLEANEVAL 2007 shared task workshop. In this paper, we present new CRF features, a handy annotation tool, and new evaluation metrics.
Evaluation itself is performed on a random sample of web pages automatically downloaded from the Czech web domain.