Nan He

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This paper presents two word segmenta-tion (WS) systems and a named entity recognition (NER) system in France Telecom R&D Beijing. The one system of WS is for open tracks based on n-gram language model and another one is for closed tracks based on maximum en-tropy approach. The NER system uses a hybrid algorithm based on Class-based language model and(More)
work, applies a nonlinear transformation from the input space to the hidden space. The output layer Partial face images, e.g.1 eyes, nose, and ear supplies the response of the network to the activa-images are significant for face recognition. In this tion pattern. paper, we present a method for partial face extraction and recognition based on Radial Basis(More)
Chinese word segmentation (CWS) lays the essential foundation for Mandarin Chinese analysis. However, its performance is always limited by the identification of unknown words, especially for short text such as Micro-blog. While local context are helpless in handling unknown words, global context do manifest enough contextual information, and could be used(More)
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