Multi-document person name resolution focuses on the problem of determining if two instances with the same name and from different documents refer to the same individual. We present a two-step approach in which a Maximum Entropy model is trained to give the probability that two names refer to the same individual. We then apply a modified agglomerative clustering technique to partition the instances according to their referents.
Fleischman and Hovy REF use a maximum entropy classifier trained on the ACL data set to give the probability that two names refer to the same referent.
Multi-Document Person Name Resolution
Conference On Reference Resolution And Its Applications
Computer Science