Open Event Extraction of Emerging Events within Sparse and Fragmented Cybercrime and Technical Online Forums

稀疏且分散的网络犯罪和技术在线论坛中的新兴事件的开放事件提取

• 批准号: 2481042
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2481042
• 关键词: Open; Event; Extraction; Emerging; Events

Novel Natural Language Processing (NLP) approaches will be developed to advance the state of the art of Open Event Extraction in the context of specialist technical online forums (e.g. cybercrime forums) where conversations around new events can be sparse, fragmented and involve out of vocabulary (e.g. new names for cybercrime products). Event schema induction and few/zero shot learning algorithms will be explored. A novel deep learning model for open event extraction will be developed, and the importance of different techniques and parameters (e.g. predefined word embeddings, attention model, few shot algorithm) will be evaluated and compared to well-known benchmark algorithms in the open event extraction literature. Performance across a set of specialist domain datasets (e.g. cybercrime, medical, legal) will be evaluated to explore how results generalize between domains. Opportunities will be available via the supervisor to present results to the National Crime Agency (NCA) as a potential avenue to impact. A Criminology academic has agreed to act as an advisor on cybercrime issues.

将开发新颖的自然语言处理（NLP）方法，以在专业技术在线论坛（例如网络犯罪论坛）的背景下推进开放事件提取的最新技术水平，在这些论坛中，围绕新事件的对话可能是稀疏的，分散的，并且涉及词汇（例如网络犯罪产品的新名称）。将探索事件模式归纳和少/零镜头学习算法。将开发一种用于开放事件提取的新型深度学习模型，并评估不同技术和参数（例如预定义的词嵌入，注意力模型，少拍算法）的重要性，并与开放事件提取文献中的知名基准算法进行比较。将评估一组专业领域数据集（例如网络犯罪、医疗、法律的）的性能，以探索结果如何在领域之间推广。将有机会通过监督员向国家犯罪局（NCA）提交结果，作为产生影响的潜在途径。一名犯罪学学者同意担任网络犯罪问题顾问。