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Currently most of state-of-the-art methods for Chinese word segmentation are based on supervised learning, whose features are mostly extracted from a local context. These methods cannot utilize the long distance information which is also crucial for word segmentation. In this paper, we propose a novel neural network model for Chinese word segmentation,(More)
The tasks in fine-grained opinion mining can be regarded as either a token-level sequence labeling problem or as a semantic compositional task. We propose a general class of discriminative models based on recurrent neural networks (RNNs) and word embeddings that can be successfully applied to such tasks without any task-specific feature engineering effort.(More)
The groundbreaking discovery of induced pluripotent stem cells (iPS cells) provides a new source for cell therapy. However, whether the iPS derived functional lineages from different cell origins have different immunogenicity remains unknown. It had been known that the cells isolated from extra-embryonic tissues, such as umbilical cord mesenchymal cells(More)
Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi-task learning framework to jointly learn across multiple related(More)
Signal processing on graph is attracting more and more attention. For a graph signal in the low-frequency space, the missing data associated with unsampled vertices can be reconstructed through the sampled data by exploiting the smoothness of graph signal. In this paper, two local-set-based iterative methods are proposed to reconstruct ban-dlimited graph(More)
As a key factor for cell pluripotent and self-renewing phenotypes, SOX2 has attracted scientists' attention gradually in recent years. However, its exact effects in dental pulp stem cells (DPSCs) are still unclear. In this study, we mainly investigated whether SOX2 could affect some biological functions of DPSCs. DPSCs were isolated from the dental pulp of(More)
Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, it is still a challenge task to model long texts, such as sentences and documents. In this paper, we propose a multi-timescale long short-term memory (MT-LSTM) neu-ral network to model long texts. MT-LSTM partitions the hidden states of the(More)
In this paper, we propose to use a discriminative training(DT) method to improve naive Bayes classifiers in context of natural language call routing. As opposed to the traditional maximum likelihood estimation, all conditional probabilties in Naive Bayes classifers (NBC) are estimated discriminatively based on the minimum classification error (MCE)(More)