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    Efficient long-distance relation extraction with DG-SpanBERT

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    Type
    Preprint
    Authors
    Chen, Jun cc
    Hoehndorf, Robert cc
    Elhoseiny, Mohamed cc
    Zhang, Xiangliang cc
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    King Abduallah University of Science and Technology Thuwal 23955, Saudi Arabia.
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020-04-07
    Permanent link to this record
    http://hdl.handle.net/10754/662553
    
    Metadata
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    Abstract
    In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBERT) that extracts semantic features from a raw sentence using the pre-trained language model SpanBERT and a graph convolutional network to pool latent features. Our DG-SpanBERT model inherits the advantage of SpanBERT on learning rich lexical features from large-scale corpus. It also has the ability to capture long-range relations between entities due to the usage of GCN on dependency tree. The experimental results show that our model outperforms other existing dependency-based and sequence-based models and achieves a state-of-the-art performance on the TACRED dataset.
    Publisher
    arXiv
    arXiv
    2004.03636
    Additional Links
    https://arxiv.org/pdf/2004.03636
    Collections
    Bio-Ontology Research Group (BORG); Preprints; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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