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| Content Provider | ACM Digital Library |
|---|---|
| Author | Agichtein, Eugene Ganti, Venkatesh |
| Abstract | 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. |
| Starting Page | 20 |
| Ending Page | 29 |
| Page Count | 10 |
| File Format | |
| ISBN | 1581138881 |
| DOI | 10.1145/1014052.1014058 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2004-08-22 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Text management Data cleaning Machine learning Information extraction Text segmentation |
| Content Type | Text |
| Resource Type | Article |
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