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5,421 results on '"National Natural Science Foundation of China"'

5. CD22/CD19 CAR-T and Auto-HSCT Sandwich Strategy as Consolidation Therapy for B-ALL

Author

National Natural Science Foundation of China(Grant No. 81970138), Jiangsu Province Natural Science Foundation of China (Grant No. BK20210091), Jining Medical University, The Second People's Hospital of Huai'an, First Affiliated Hospital Bengbu Medical College, Northern Jiangsu People's Hospital, Affiliated Hospital of Nantong University, Suzhou Hospital of Traditional Chinese Medicine, and Sheng-Li Xue, MD, Professor

Published
2024

32. Event Recognition Based on Deep Learning in Chinese Texts.

Author

Yajun Zhang, Zongtian Liu, Wen Zhou, and National Natural Science Foundation of China (NSFC)

Subjects
Medicine, Science
Abstract

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

Published
2016
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