# BVTED: A Specialized Bilingual (Chinese-English) Dataset for Vulnerability Triple Extraction Tasks
## 2 Related Work
### Information Extraction Techniques in Cybersecurity
#### 2.2.2 NER in Cybersecurity  
Relationship extraction to extract the semantic relationship between entities and the formal description as a relationship triplet form, namely, (entity 1, relationship, entity 2), stored in the knowledge base, utilizes many research-based methods, which overall can be divided into two categories: those based on a knowledge engineering method and those based on machine learning [22]. Since 1998, following the MUC-7 meeting, the entity relationship extraction task, involved relationship extraction mainly using artificial build knowledge engineering [23], including annotation referring to information, write extraction rules, relationship templates, etc., but the method exhibits low efficiency and has poor portability. In addition, with an increased number of rules, there may also be conflict between different rules, creating later maintenance difficulties. Therefore, machine learning-based relationship extraction techniques have emerged. At present, according to the different machine learning algorithms used, they can be divided into three methods based on feature vectors, kernel functions and neural networks. [PERSON] [24], because of the lack of a vertical annotation corpus and the traditional extraction method relationship overlap problem, proposed a threat intelligence entity relationship joint extraction model based on the BERT network. The method combines the BERT training language model and introduces rich semantic information, enabling entity recognition and relationship extraction using two sub-task parameter sharing. [PERSON] [2] proposed the threat intelligence information extraction method (TIIE) based on a neural network model. On the one hand, the long-short memory neural network (LSTM) is used to perform the named entity recognition task, while the conditional random field (CRF) model is used to realize the constraint between sequence tags. On the other hand, the relationship extraction task is combined with the long-short memory neural network model and the shortest dependence path (SDP) method. [PERSON] et al. [25] proposed Vulcan (a CTI system) to identify various types of descriptive or static CTI data from unstructured text and to determine the semantic relationship between them. An NER model based on the language model was designed as a threat entity identifier (TEI) to learn the context information of the words, so as to identify the named entities related to ransomware attacks in unstructured text, and through the entity linker to understand the hidden meaning between entities, such as where a single entity may represent different things, or multiple entities with different names represent the same thing. [PERSON] et al. [26] proposed processing news text in the cybersecurity field by combining information extraction and emotion analysis. This method divides information extraction into two stages: first, keywords are selected using the tf-idf weighting method, and then are used to filter the context of the selected word through the word2 vec model. Affective analysis is divided into supervised and unsupervised (vocabulary-based) methods. The former captures the context information of text data through the N-gram feature extraction algorithm, while the latter is suitable for situations where there are no labeled data. [PERSON] [17] proposed the joint extraction method of entity relationship based on relationship decomposition. Firstly, it performs the multi-relationship classification task through the encoder, and then generates triples by identifying the entities under a specific relationship. By capturing the semantic interdependence between entity and relationship extraction steps, this method can effectively solve the triplet overlap problem and reduce the interference of unrelated entity pairs. [PERSON] [19] and others, based on semantic role annotation relationship extraction, put forward the RE-SRL algorithm.