Iteratively Estimating Pattern Reliability and Seed Quality With Extraction Consistency

In this paper, we focus on the task of distilling relation instances from the Web. Most of the approaches for this task were based on provided seed instances or patterns to initiate the process. Thus, the result of the extraction depends largely on the quality of the instances and patterns. For this matter, we propose an iterative mechanism that estimates the reliability of a pattern by the consistency of its extractions, and reevaluate the usefulness of seed instance based on estimated pattern reliability. The resulting system is a semi-supervised method that can take a large quantity of seed instances with diverse quality. To evaluate the effectiveness of our approach, we experimented on 8 types of relationships. The empirical results show that our system performs quite consistency in different relationships while maintaining high precision and recall value.