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• W243668 abstract "The last few years have seen an explosion in the amount of text becoming available on the World Wide Web as online communities of users in diverse domains emerge to share documents and other digital resources. In this paper we explore the issue of how to provide a low-level information extraction tool based on hidden Markov models that can identify and classify terminology based on previously marked-up examples. Such a tool should provide the basis for a domain portable information extraction system, that when combined with search technology can help users to access information more effectively within their document collections than today's information retrieval engines alone. We present results of applying the model in two diverse domains: news and molecular biology and discuss the model and term markup issues that this investigation reveals." @default.
• W243668 created "2016-06-24" @default.
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• W243668 date "2001-05-01" @default.
• W243668 modified "2023-09-23" @default.
• W243668 title "Machine learning for information extraction from XML marked-up text on the semantic web" @default.
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