The task of Cross-Document Co-reference Resolution (CDCR) aims to merge those words distributed in different texts which refer to the same entity together to form co-reference chains. The traditional research on CDCR addresses name disambiguation posed in information retrieval using clustering methods. This paper transformed CDCR as a classification problem by using an Support Vector Machine (SVM) classifier to resolve both name disambiguation and variant consolidation, both of which were prevalent in information extraction. This method can effectively integrate various features, such as morphological, phonetic, and semantic knowledge collected from the corpus and the Internet. The experiment on a Chinese cross-document co-reference corpus shows the classification method outperforms clustering methods in both precision and recall.

In order to solve the problem of ensuring the needs of diversified services in Named Data Networking (NDN), Content Delivery Model based on Polymorphic Routing (CDMPR) was proposed. In this model, four types of reconfigurable modular combination, namely content request, routing, table lookup, and content cache, were used to carry the service transmission with different characteristic requirements. The polymorphic routing algorithm examples for real-time traffic, non-real-time streaming media services and user generated services were designed in detail, including improved ant colony optimization routing algorithm, the routing strategy of tracking and the shortcut routing mechanism based on oriented copy announcement. Simulation results show that compared with Diverse Content Delivery Scheme based on traffic types (DCDS) algorithm, CDMPR improves the average cache hit rate of network nodes by 4.45%-13.8%; compared with Content-Centric Networking (CCN) algorithm, the average response latencies of three types of services are reduced by 29.17%-53.02%, respectively. CDMPR realizes the support for difference service requirements.