Finding light in dark archives: using AI to connect context and content in email

Finding light in dark archives: using AI to connect context and content in email
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在黑暗档案中寻找光明:使用人工智能连接电子邮件中的上下文和内容

DOI:
10.1007/s00146-021-01369-9
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发表时间:
2021
期刊:
影响因子:
3
通讯作者:
Decker S
Decker S
中科院分区:
--
文献类型:
--
作者:
Decker S

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电子邮件档案是重要的历史资源,但访问此类数据提出了独特的档案挑战,许多天生的数字馆藏仍然是黑暗的,而如何有效地提供它们的问题仍然存在。本文通过满足用户的需求,促进了人们对保留对电子邮件的访问日益增长的兴趣,为此类集合变得更广泛可用做好准备。我们认为,为了有效地访问电子邮件内容,电子邮件的上下文必须构成此访问的一部分。在探索这个想法时,我们专注于在组织电子邮件的大型、多托管档案中进行发现,其中电子邮件的网络特征尤其明显。我们介绍了我们的原型搜索工具,它使用基于人工智能的方法来支持用户驱动的电子邮件探索。具体来说,我们集成了两种不同的人工智能模型,它们系统地生成不同类型的结果,一种基于简单的短语匹配,另一种基于更复杂的 BERT 嵌入。总之,这些为情境发现提供了一条新途径,考虑到未来档案用户、他们的兴趣和经验水平的多样性。
Email archives are important historical resources, but access to such data poses a unique archival challenge and many born-digital collections remain dark, while questions of how they should be effectively made available remain. This paper contributes to the growing interest in preserving access to email by addressing the needs of users, in readiness for when such collections become more widely available. We argue that for the content of email to be meaningfully accessed, the context of email must form part of this access. In exploring this idea, we focus on discovery within large, multi-custodian archives of organisational email, where emails’ network features are particularly apparent. We introduce our prototype search tool, which uses AI-based methods to support user-driven exploration of email. Specifically, we integrate two distinct AI models that generate systematically different types of results, one based upon simple, phrase-matching and the other upon more complex, BERT embeddings. Together, these provide a new pathway to contextual discovery that accounts for the diversity of future archival users, their interests and level of experience.
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