document: Mining reference tables for automatic text segmentation Automatically segmenting unstructured text strings into structured records is necessary for importing the information contained in legacy sources and text collections into a data warehouse for subsequent querying , analysis , mining and integration . In this paper , we mine tables present in data warehouses and relational databases to develop an automatic segmentation system . Thus , we overcome limitations of existing supervised text segmentation approaches , which require comprehensive manually labeled training data . Our segmentation system is robust , accurate , and efficient , and requires no additional manual effort . Thorough evaluation on real datasets demonstrates the robustness and accuracy of our system , with segmentation accuracy exceeding state of the art supervised approaches .
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