Cybersecurity Transformer Entity-Relation Extraction With Distant Labels
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Solution Overview
Problem
Current natural language processing (NLP) technologies face challenges in performing cybersecurity-specific named entity recognition and relation extraction due to the lack of publicly available training data with manually annotated labels, and the presence of specialized terms and jargon in cybersecurity text that are not handled effectively by general domain NLP models.
Innovation Solution
The method involves adapting a pretrained language model, such as GPT-2, by introducing it to a corpus of security text and using unsupervised training to generate a fine-tuned model. This adapted model is then combined with a joint extraction model to perform entity and relation label prediction. Additionally, distant labels are applied to the security text, and distant supervision training is used to transform the model into a Security Language Model capable of NER and RE.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning is used for relation extraction with hand-labeled corpora, then extraction accuracy is improved, but training data production cost increases
Solution Approach 1:
The system uses automated distant supervision to generate training labels from existing knowledge bases and text corpora without requiring manual annotation. The model extracts relations automatically by leveraging co-occurrence patterns and knowledge base assertions, making the training data production self-service rather than relying on expensive human labeling.
Solution Approach 2:
The approach copies labeled data patterns from knowledge bases and applies them to unannotated text through distant supervision. Instead of creating new manual labels, the system replicates labeling patterns from existing structured data sources to automatically annotate training corpora.
2Measurement precision
If domain-specific training data is used, then extraction precision for that domain is improved, but model adaptability to other domains decreases
Solution Approach 1:
The system performs preliminary domain adaptation by fine-tuning the language model on domain-specific text corpora before relation extraction. This preliminary action adapts the model to domain terminology and patterns while maintaining the underlying architecture's generality, enabling subsequent adaptation to other domains if needed.
Solution Approach 2:
The approach applies domain-specific processing locally through fine-tuning on domain corpora and using domain-adapted entity recognizers, while the core relation extraction framework remains general-purpose. This allows domain-specific precision where needed without compromising overall model versatility.
3Adaptability or versatility
If general domain NLP models are used, then model versatility is improved, but handling of specialized cybersecurity terminology decreases
Solution Approach 1:
The system performs preliminary adaptation by fine-tuning the general domain language model on cybersecurity-specific text corpora. This preliminary action adjusts the model's vocabulary and contextual understanding to cybersecurity terminology while preserving the model's overall versatility for other tasks.
Solution Approach 2:
The approach uses domain-specific text corpora and knowledge bases as intermediaries to bridge general domain models and specialized cybersecurity applications. These intermediaries transfer domain-specific knowledge to the model without requiring complete retraining, maintaining versatility while improving specialized handling.
Data Source
AI summary
Systems and methods are provided for adapting a pretrained language model to perform cybersecurity-specific named entity recognition and relation extraction. The method includes introducing a pretrained language model and a corpus of security text to a model adaptor, and generating a fine-tuned language model through unsupervised training utilizing the security text corpus. The method further includes combining a joint extraction model from a head for joint extraction with the fine-tuned language model to form an adapted joint extraction model that can perform entity and relation label prediction. The method further includes applying distant labels to security text in the corpus of security text to produce security text with distant labels, and performing Distant Supervision Training for joint extraction on the adapted joint extraction model using the security text to transform the adapted joint extraction model into a Security Language Model for name-entity recognition (NER) and relation extraction (RE).