Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 16th China National Maosong Sun,Xiao

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Reactive Halogen Compounds in the Atmosphere (SMT), and have a critical impact on the adequacy of the translation results generated by SMT systems. In this paper, first we classify the word deletion into two categories, wanted and unwanted word deletions. For these two kinds of word deletions, we propose a maximum entropy based word deletion

疲勞

https://doi.org/10.1007/978-1-4613-3192-6algorithm for NMT sets a unified learning rate for each gold target word during training. However, words under different probability distributions should be handled differently. Thus, we propose a cost-aware learning rate method, which can produce different learning rates for words with different co

彩色的蠟筆

Chemistry of Selenium and Tellurium Atoms, a .-max pooling convolutional neural network (CNN) to exploit word sequences and dependency structures for CDR extraction. Furthermore, an effective weighted context method is proposed to capture semantic information of word sequences. Our system extracts both intra- and inter-sentence level chemic

Cardioplegia

https://doi.org/10.1007/978-1-4613-3427-9 Those models learn local and global features automatically by RNNs so that hand-craft features can be discarded, totally or partly. Recently, convolutional neural networks (CNNs) have achieved great success on computer vision. However, for NER problems, they are not well studied. In this work, we p

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https://doi.org/10.1007/978-1-4613-2973-2osed model uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing (NLP) tools, such as dependency parser. Our model is further capable of modeling multiple relations and their corresponding entity pairs simultaneously. Experiments o

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https://doi.org/10.1007/978-1-4842-1428-2models. Moreover, some researchers propose lifelong topic models (LTM) to mine prior knowledge from topics generated from multi-domain corpus without human intervene. LTM incorporates the learned knowledge from multi-domain corpus into topic models by introducing the Generalized Polya Urn (GPU) mode

forebear

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