An Information Extraction Method for Multiple Data Sources

We developed a method of information extraction for multiple data sources or for various kinds of datasets like Internet web pages. Generally, because many different writing styles or vocabularies exist among different kinds of data, the accuracy of information extraction using various kinds of datasets is not better than that using a single kind of data. Our method divides the data by clustering and learns extraction rules to increase accuracy even if we use various kinds of datasets. In our experiment, we applied our method to a NTCIR8 Technical Trend Map Creation subtask that uses two kinds of data, patents and technical papers, and obtained the better precision than normal information extraction method.