A Neural Relation Extraction Model for Distant Supervision in Counter-Terrorism Scenario

A Neural Relation Extraction Model for Distant Supervision in Counter-Terrorism Scenario

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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 hybrid willows for sale 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 red prairie spy apple 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.