Titlebook: Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence; 5th China Conference Huajun Chen,Kang Liu,Lei Hou Confe

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書目名稱Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence影響因子(影響力)




書目名稱Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence影響因子(影響力)學(xué)科排名




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書目名稱Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence被引頻次




書目名稱Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence被引頻次學(xué)科排名




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書目名稱Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence年度引用學(xué)科排名




書目名稱Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence讀者反饋




書目名稱Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence讀者反饋學(xué)科排名

reflection

https://doi.org/10.1007/978-981-16-1964-9artificial intelligence; character recognition; computational linguistics; computer systems; data mining

Overthrow

negligence

鬼魂

Chinese Punctuation Prediction with Adaptive Attention and Dependency Treeon prediction outperforms BiLSTM+CRF with a gain of 0.292% and 0.127% on accuracy in two datasets respectively. The second proposal outperforms existing methods with a gap of above 4.5% of accuracy and reaches state-of-the-art performance in two datasets.

painkillers

北極熊

Improving Relation Extraction Using Semantic Role and Multi-task Learning a Macro-F1 score of 89.96% on the benchmark dataset, outperforming most of the existing methods. More ablation experiments on two different datasets show that semantic role information and multi-task learning can help improve the relation extraction.

熟練

Osteoporosis

Transfusion

IBRE: An Incremental Bootstrapping Approach for Chinese Appointment and Dismissal Relation Extractioposed to get high accuracy. Finally, we augment seeds with corrected tuples and apply incremental learning to continually improve performance with least training cost. We build a dataset called ADNP (Appointment and Dismissal News from People.cn) and compare our approach with baselines. Comparison r