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Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements.

Software defined networking (SDN) makes networks programmable and application-aware by decoupling network control and management (NC&M) from data forwarding and leveraging centralized NC&M to facilitate user-customized routing and switching. Inspired by these, this paper investigates how to realize the OpenFlow-controlled (OF-controlled) network orchestration that can facilitate efficient scalable video coding (SVC) streaming to heterogeneous clients. Specifically, we consider real-time SVC streaming and address the situation in which video sources reside in geographically-distributed servers and clients can join and leave the streaming services dynamically. We formulate this as a multi-source multi-destination manycast problem and realize the networking system with an OF-controlled SDN architecture. We first design the OF controller to enable efficient network operations. Then, we focus on solving the multi-source multi-destination SVC video manycast problem and design several algorithms. Initially, an integer linear programming (ILP) model is formulated to obtain the optimal solutions for small-scale problems. Next, we try to make the manycast algorithm suitable for practical implementation, and design two time-efficient heuristics. Simulation results indicate that the heuristics can provide close-to-optimal solutions. Finally, we build an OF network testbed that consists of OF switches, SVC video servers and clients, and perform SVC streaming experiments to demonstrate our design. Experimental results verify that the proposed scheme can allocate bandwidth intelligently and ensure high-quality video streaming. To the best of our knowledge, this is the first work that accomplishes experimental demonstration of OF-controlled network orchestration for adaptive SVC video manycast.Index Terms-Manycast, openflow, scalable video coding (SVC), software-defined networking (SDN), video streaming.

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To improve the efficiency and scalability of video streaming in large scale mesh networks, we propose a cloudassisted video delivering strategy that can distribute video contents to subscribers over multiple routing paths dynamically. By leveraging the computation power and numerous storage of a cloud and the flexibility of scalable video coding (SVC) scenarios, we design algorithms for multiple routing path selection and assignment, and propose to run them in a cloud for fast processing. The multi-path routing is then achieved by labelswitching in the network layer. The simulation results show that the proposed algorithms achieved efficient usage of the network resources and improved user experience in terms of bandwidth, delay and jitter.