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）的背景下推进开放式活动提取的艺术状态，在这些背景下，围绕新事件的对话可以稀疏，分散且涉及词汇表（例如，Cyber​​crime Products的新名称）。将探索事件模式感应和很少的射击学习算法。将开发一个新颖的开放式事件提取模型，并将评估不同技术和参数的重要性（例如，预定义的单词嵌入，注意模型，很少的射击算法）并将其与开放事件提取文献中众所周知的基准测试算法进行比较。将评估一组专业领域数据集（例如网络犯罪，医疗，法律）的性能，以探讨结果如何在域之间推广。主管将提供机会，向国家犯罪局（NCA）提出结果，以此作为影响的潜在途径。犯罪学的学者同意担任网络犯罪问题的顾问。