Spotting The 'Odd-One-Out': Data-Driven Error Detection And Correction In Textual Databases

We present two methods for semiautomatic detection and correction of errors in textual databases. The first method (horizontal correction) aims at correcting inconsistent values within a database record, while the second (vertical correction) focuses on values which were entered in the wrong column. Both methods are data-driven and language-independent. We utilise supervised machine learning, but the training data is obtained automatically from the database; no manual annotation is required. Our experiments show that a significant proportion of errors can be detected by the two methods. Furthermore, both methods were found to lead to a precision that is high enough to make semi-automatic error correction feasible.