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- W133766174 abstract "The World Wide Web is a treasure trove of information. The Web’s sheer scale m,l~.s automatic location and extraction of information appealing. However, much of the information lies bmied in documents designed for human consumption, such as home pages or product ~ta_Sogs. Before software agents can extract nuggets of infonnmion fi’om Web documents, they have to be able to recognize it despite the multitude of formats in wh/ch it may appear. In this paper, we take a machine learning approach to the problem. We explain why existing grammar inference techniques face difficulties in this domain, present a new techn/que, and demonstrate its success on examples drawn f~om the Web ranging f~om CMU Tech Report codes to bus schedules. Our algorithm is shown to learn target languages found on the Web in si~mlfw.aufly fewer examples than Inevious methods. In addition, our algmiH~n is guaranteed to learn in the limit, and rims in time OOS~, where ISI is the size of the sample." @default.
- W133766174 created "2016-06-24" @default.
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- W133766174 date "2002-01-01" @default.
- W133766174 modified "2023-09-23" @default.
- W133766174 title "A Grammar Inference Algorithm for the World Wide Web" @default.
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