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What is GHMM for Web Information Extraction about?

This paper presents a Generalized Hidden Markov Model (GHMM) for Web information extraction, which enhances traditional HMMs by utilizing Web-specific features such as content blocks, layout structures, and multiple emission attributes. The GHMM approach aims to improve the accuracy of information extraction by recognizing the logical grouping of content and the two-dimensional nature of web pages. Experimental results indicate that the GHMM outperforms conventional HMMs in extracting relevant information f

Author

Bernardino Surexi

Language

EN