Enhancing Usability of Information Extraction Results with Textual Data Profiling

Given a targeted subject and a text collection, information extraction techniques provide the capability to populate a database in which each record entry is a subject instance documented in the text collection. However, even with the state-of-the-art IE techniques, IE task results are expected to contain errors. Manual error detection and correction are labor intensive and time consuming. This validation cost remains a major obstacle to actual deployment of practical IE applications with high validity requirement. In this paper, we propose a string feature-based approach to textual data profiling and invalid data detection. The approach is based on the observation that values of an attribute in IE results are symbolic form variations of a concept in the IE task subject and may exhibit a certain congruity with some string features. We conducted experiments to verify that effective detection of IE invalid values can be achieved by using the surface-form string features.