A Neural Networks-Based graph algorithm for cross-document coreference resolution

Cross-document coreference resolution, which is an important subtask in natural language processing systems, focus on the problem of determining if two mentions from different documents refer to the same entity in the world. In this paper we present a two-step approach, employing a classification and clusterization phase. In a novel way, the clusterization is produced as a graph cutting algorithm, namely, neural networks-based BestCut (NBCut). To our knowledge, our system is the first that employs a statistical model in graph partitioning. We evaluate our approach on ACE 2008 cross-document coreference resolution data sets and obtain encouraging result, indicating that on named noun phrase coreference task, the approach holds promise and achieves competitive performance.