Charset codes webtools7/3/2023 In: Proceedings of the 16th Conference on Computational linguistics, vol. Kikui, G.I.: Identifying, the coding system and language, of on-line documents on the internet. ICU - International Components for Unicode, IBM (2014). Tang, F.Y.F.: Mozilla Charset Detectors, Mozilla (2008). Kim, S., Park, J.: Automatic Detection of Character Encoding and Language, Technical Report, Machine Learning, Stanford University (2007) In: Rapport Scientifique, Laboratoire de Recherche Appliquée en Linguistique Informatique (RALI), Université de Montréal, Canada (2003) Russell, G., Lapalme, G., Plamondon, P.: Automatic identification of language and encoding. Results show that the proposed technique significantly improves the accuracy of charset encoding detection over both Mozilla CharDet and IBM ICU. In the Ensemble Classification phase, we leverage two well-known charset encoding detection tools, namely Mozilla CharDet and IBM ICU, and combine their outputs based on their estimated domain of expertise. Therefore, HTML markups and other structural data such as scripts and styles are separated from the rendered texts of the HTML documents using a decoding-encoding trick which preserves the integrity of the byte sequence. The Markup Elimination phase is based on the hypothesis that charset encoding detection is more accurate when the markups are removed from the main content. Our approach consists of two phases: “Markup Elimination” and “Ensemble Classification”. In this paper, we present a new hybrid technique for charset encoding detection for HTML documents. Charset encoding detection is a primary task in various web-based systems, such as web browsers, email clients, and search engines.
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