Data Cleaning publication categorizer

Traditionally, record linkage algorithms have played an important role in maintaining digital libraries - i.e., identifying matching citations or authors for consolidation in updating or integrating digital libraries. As such, a variety of record linkage algorithms have been developed and deployed successfully. Often, however, existing solutions have a set of parameters whose values are set by human experts off-lineand are fixed during the execution. Since finding the ideal values of such parameters is not straightforward, or no such single ideal value even exists, the applicability of existing solutions to new scenarios or domains is greatly hampered. To remedy this problem, we argue that one can achieve significant improvement by adaptively and dynamically changing such parameters of record linkage algorithms. To validate our hypothesis, we take a classical record linkage algorithm, the sorted neighborhood method (SNM), and demonstrate how we can achieve improved accuracy and performance by adaptively changing its fixed sliding window size. Our claim is analytically and empirically validated using both real and synthetic data sets of digital libraries and other domains.