An XML-Based Bootstrapping Method for Pattern Acquisition

نویسندگان

  • Xingjie Zeng
  • Fang Li
  • Dongmo Zhang
  • Athena Vakali
چکیده

Extensible Markup Language (XML) has been widely used as a middleware because of its flexibility. Fixed domain is one of the bottlenecks of Information Extraction (IE) technologies. In this paper we present a XML-based domain-adaptable bootstrapping method of pattern acquisition, which focuses on minimizing the cost of domain migration. The approach starts from a seed corpus with some seed patterns; extends the corpus based on the seed corpus through the Internet and acquires the new patterns from extended corpus. Positive and negative examples classified from training corpus are used to evaluate the patterns acquired. The result shows our method is a practical way in pattern acquisitions.

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تاریخ انتشار 2004