期刊
IEEE ACCESS
卷 8, 期 -, 页码 225088-225096出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3042672
关键词
Bit error rate; Data mining; Encoding; Feature extraction; Data models; Big Data; Training; BERT; relation extraction; distant supervision; selective attention mechanism; BERT entity encoding
资金
- National Key Research and Development Program of China [2017YFC0803700]
- People's Public Security University of China through 2019 Basic Research Operating Expenses New Teacher Research Startup Fund Project [2019JKF424]
- Natural Science Foundation of China [41971367]
Natural language processing (NLP) is the best solution to extensive, unstructured, complex, and diverse network big data for counter-terrorism. Through the text analysis, it is the basis and the most critical step to quickly extract the relationship between the relevant entities pairs in terrorism. Relation extraction lays a foundation for constructing a knowledge graph (KG) of terrorism and provides technical support for intelligence analysis and prediction. This paper takes the distant-supervised relation extraction as the starting point, breaks the limitation of artificial data annotation. Combining the Bidirectional Encoder Representation from Transformers (BERT) pre-training model and the sentence-level attention over multiple instances, we proposed the relation extraction model named BERT-att. Experiments show that our model is more efficient and better than the current leading baseline model over each evaluative metrics. Our model applied to the construction of anti-terrorism knowledge map, it used in regional security risk assessment, terrorist event prediction and other scenarios.
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