Joint extraction of entities and relations using graph convolution over pruned dependency trees
| Autor: | Kaiwen Zhang, Yanxia Liu, Jianjun Hu, Yin Hong, Suizhu Yang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Source code Artificial neural network business.industry Computer science Cognitive Neuroscience media_common.quotation_subject Pattern recognition 02 engineering and technology Relationship extraction Graph Computer Science Applications 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence business Sentence media_common |
| Zdroj: | Neurocomputing. 411:302-312 |
| ISSN: | 0925-2312 |
| Popis: | We present a novel end-to-end deep neural network model based on graph convolutional networks for simultaneous joint extraction of entities and relations among them. Our model captures context and syntactic information from sentence by stacking a graph convolutional layer over bidirectional sequential LSTM layers. We sequentially concatenate the subject, object and sentence representations for obtaining the directionality of relations. Besides, in order to address long entity-distances problem, we further apply a path-centric pruning procedure to input trees in order to preserve useful information while maximally removing irrelevant words. Experiments are conducted on NYT dataset, and the proposed model achieves the state-of-the-art results on entity and relation extraction task. Our source code is available on Github: https://github.com/michael-hon/LSTM-GCN-ER. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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