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.
Maximum-Entropy Min-Cut BestCut Neural-Networks NBCut
Saike HE Yuan DONG Haila WANG
School of Information Engineering of Beijing University of Posts and Telecommunications,100876 Beiji France Telecom R&D Center,100080;Beijing University of Posts and Telecommunications,100876 Beijing,C France Telecom R&D Center,100080,Beijing,China
国际会议
北京
英文
2008-10-19（万方平台首次上网日期，不代表论文的发表时间）