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

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Employing Auto-annotated Data for Person Name Recognition in Judgment Documents In this paper, we focus on person name recognition in judgment documents. Owing to the lack of human-annotated data, we propose a joint learning approach, namely Aux-LSTM, to use a large scale of auto-annotated data to help human-annotated data (in a small size) for person name recognition. Specifi

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Closed-Set Chinese Word Segmentation Based on Convolutional Neural Network Modell to each character, indicating its relative position within the word it belongs to. To do so, it first constructs shallow representations of characters by fusing unigram and bigram information in limited context window via an element-wise maximum operator, and then build up deep representations fro

opprobrious

泥土謙卑

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極小量

預(yù)測

Cost-Aware Learning Rate for Neural Machine Translationalgorithm 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

tackle

Integrating Word Sequences and Dependency Structures for Chemical-Disease Relation Extraction 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