Deep Learning for Natural Language Processing: A Survey

Arkhangelskaya, E. O.; Nikolenko, S. I.

doi:10.1007/s10958-023-06519-6

Cited by 5 publications

(1 citation statement)

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“…This technology possesses robust capabilities in information extraction, text understanding, and semantic analysis, enabling it to identify and label crucial concepts and entities from unstructured text, providing a solid foundation for automatic semantic generation. Long short-term memory neural networks (LSTMs), which are a form of recurrent neural networks (RNNs), are widely used in NLP [18][19][20][21]. RNNs face issues such as gradient vanishing and exploding when dealing with long sequences, making it difficult to capture long-term dependencies.…”

Section: Related Workmentioning

confidence: 99%

ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies

Zhang,

Liang

2024

Applied Sciences

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Semantic generation of network access control policies can help network administrators accurately implement policies to achieve desired security objectives. Current semantic generation research mainly focuses on semantic generation of single grammar and lacks work on automatically generating semantics for different grammatical strategies. Generating semantics for different grammars is a tedious, inefficient, and non-scalable task. Inspired by sequence labeling in the field of natural language processing, this article models automatic semantic generation as a sequence labeling task. We propose a semantic generation algorithm named ADLBiLSTM. The algorithm uses a self-attention mechanism and double-layer BiLSTM to extract the features of security policies from different aspects, so that the algorithm can flexibly adapt to policies of different complexity without frequent modification. Experimental results showed that the algorithm has good performance and can achieve high accuracy in semantic generation of access control list (ACL) and firewall data and can accurately understand and generate the semantics of network access control policies.

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Section: Related Workmentioning

confidence: 99%