Information extraction based on probing algorithm with Bayesian approach

J. Davidson, I. Jacob, K. G. Srinivasagam
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Abstract

Document Annotation is the task of adding metadata information in the document which is useful in information extraction. Document annotation has emerged as a different stream in data mining. Majority of algorithms are concentrated on query workload. This paper uses Probing algorithm with Bayesian approach which identifies the attribute based on query workload, text frequency and content of the previous text annotation such as content value. This method has been implemented in datasets that facilitates data annotation and prioritizes the values of the attributes by ranking scheme. Query cost is also low when compared to other approach. The experimental analysis shows a better performance while comparing with other methods because probability theory provides a principled foundation for such reasoning under uncertainty.
基于贝叶斯方法探测算法的信息提取
文档注释是在文档中添加元数据信息的任务,它对信息提取非常有用。文档注释已经成为数据挖掘中一个不同的流。大多数算法都集中在查询工作负载上。本文采用基于贝叶斯方法的探测算法,根据查询工作量、文本频率和前一个文本注释的内容(如内容值)来识别属性。该方法已在数据集中实现,方便了数据标注,并通过排序方案对属性值进行优先级排序。与其他方法相比,查询成本也很低。实验分析表明,与其他方法相比,概率论为这种不确定情况下的推理提供了原则性的基础。
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