Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking.

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With the rapidly increasing number of mobile devices being used as essential terminals or platforms for communication, security threats now target the whole telecommunication infrastructure and become increasingly serious. Network probing tools, which are deployed as a bypass device at a mobile core network gateway, can collect and analyze all the traffic for security detection. However, due to the ever-increasing link speed, it is of vital importance to offload the processing pressure of the detection system. In this paper, we design and evaluate a real-time pre-processing system, which includes a hardware accelerator and a multi-core processor. The implemented prototype can quickly restore each encapsulated packet and effectively distribute traffic to multiple back-end detection systems. We demonstrate the prototype in a well-deployed network environment with large volumes of real data. Experimental results show that our system can achieve at least 18 Gb/s with no packet loss with all kinds of communication protocols.

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