# BVTED: A Specialized Bilingual (Chinese-English) Dataset for Vulnerability Triple Extraction Tasks
## 2 Related Work
### Information Extraction Techniques in Cybersecurity
#### 2.2.3 Joint Entity and Relation Extraction in Cybersecurity  
The traditional assembly line (pipeline) extraction method involves entity relationship extraction for entity recognition and relationship classification of two independent sub-tasks, namely, on the basis of complete entity recognition and relationship classification. The method is easy to implement, but due to the lack of interaction between the two sub-tasks, cannot capture the internal connection and dependency between tasks, increasing the redundant information and computational complexity. In addition, since the follow-up tasks are completed on the basis of the presequence, the accuracy of the follow-up tasks largely depends on the completion of the presequence, so it will also bring problems such as error accumulation and error propagation. Therefore, it is a new research trend to establish a unified model to complete entity recognition and relationship classification from unstructured or semi-structured text. At present, the methods of joint extraction of supervised entities mainly include joint extraction based on feature engineering and joint extraction based on neural networks [27].  
[PERSON] et al. [28] proposed the joint learning framework TIRECO, which is a model that can complete the extraction and resolution of threat intelligence relations simultaneously. It considers the co-reference relationship as a predefined relationship and completes the graph convolution network (GCN) model. For the problem that document-level text is too long to extract features, the concept of sentence set is proposed, which considers relationship extraction as a multi-classification task at the sentence level, and fully captures the syntax dependency information based on a shortest dependency path pruning strategy (SDP-VP-SET). [PERSON] et al. [29], in the implementation of joint extraction into the dynamic attention mechanism, and for the lack of a corpus, based on the idea of active learning, introduced the confrontation learning mechanism. They put forward a kind of active learning framework as a corpus sampling method, marking high quality data using incremental screening and iterative training to improve the performance of the joint extraction model. [PERSON] et al. [30] proposed a joint learning threat intelligence entity recognition extraction method, seeking to address pipeline learning error transmission, information loss and redundancy issues. The method involves parameter sharing, entity recognition and relationship classification, combining two separate tasks into a sequence annotation problem, at the same time identifying the entity and relationship, to avoid the characteristics of complex engineering. [PERSON] et al. [31] put forward a joint entity and relation extraction model with regard to cybersecurity, namely, CyberRel. It generates separate label sequences for different relationships. Joint extraction problem modeling is understood as a multiple sequence tag problem, with each label sequence containing the entities involved in the text and the subject and object information. On the one hand, it effectively solves the common entity overlap problem in the cybersecurity corpus. On the other hand, it also avoids the error propagation associated with the pipeline method. [PERSON] et al. [32] proposed an end-to-end joint extraction model suitable for the field of cybersecurity intelligence. It first models entity and relationship extraction as a serialized labeling task through a joint labeling strategy, and then constructs an end-to-end sequence labeling model based on BERT-ATT-BiLSTM-CRF. Finally, it involves a new entity and relationship matching rule for the relational overlap problem to extract knowledge triples.