• Refine your search:
  • 
    Keyword
  • 
    Creator / Publisher:
    
    • Huang, Ming (1)
    • Li, Dingcheng (1)
    • Li, Xiaodi (1)
    • Ruan, Yaoping (1)
    • Yao, Lixia (1)
  • 
    Year
  • 
    Medium:
    
    • Online (1)
  • 
    Type
  • 
    BLLDB-Access

Search in the Catalogues and Directories

All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 MfeCNN: Mixture Feature Embedding Convolutional Neural Network for Data Mapping Li, Dingcheng; Huang, Ming; Li, Xiaodi; Ruan, Yaoping; Yao, Lixia. - 2018 Abstract: Data mapping plays an important role in data integration and exchanges among institutions and organizations with different data standards. However, traditional rule-based approaches and machine learning methods fail to achieve satisfactory results for the data mapping problem. In this paper, we propose a novel and sophisticated deep learning framework for data mapping called mixture feature embedding convolutional neural network (MfeCNN). The MfeCNN model converts the data mapping task to a multiple classification problem. In the model, we incorporated multimodal learning and multiview embedding into a CNN for mixture feature tensor generation and classification prediction. Multimodal features were extracted from various linguistic spaces with a medical natural language processing package. Then, powerful feature embeddings were learned by using the CNN. As many as 10 classes could be simultaneously classified by a softmax prediction layer based on multiview embedding. MfeCNN achieved the best results on unbalanced data (average F1 score, 82.4%) among traditional state-of-the-art machine learning models and CNN without mixture feature embedding. Our model also outperformed a very deep CNN with 29 layers, which took free texts as inputs. The combination of mixture feature embedding and a deep neural network can achieve high accuracy for data mapping and multiple classification. Keyword: Article URL: https://doi.org/10.1109/TNB.2018.2841053 http://www.ncbi.nlm.nih.gov/pubmed/29993581 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6118402/ BASE Hide details

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 0 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 0 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 0 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 1 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0