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Assisting the appraisal of e-mail records with automatic classification

机译：通过自动分类协助评估电子邮件记录

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Purpose - This paper aims to investigate how automatic classification can assist employees and records managers with the appraisal of e-mails as records of value for the organization. Design/methodology/approach - The study performed a qualitative analysis of the appraisal behaviours of eight records management experts to train a series of support vector machine classifiers to replicate the decision process for identifying e-mails of business value. Automatic classification experiments were performed on a corpus of 846 e-mails from two of these experts' mailboxes. Findings - Despite the highly contextual nature of record value, these experiments show that classifiers have a high degree of accuracy. Unlike existing manual practices in corporate e-mail archiving, machine classification models are not highly dependent on features such as the identity of the sender and receiver or on threading, forwarding or importance flags. Rather, the dominant discriminating features are textual features from the e-mail body and subject field. Research limitations/implications - The need to automatically classify corporate e-mails is growing in importance, as e-mail remains one of the prevalent recordkeeping challenges. Practical implications - Automated methods for identifying e-mail records promise to be of significant benefit to organizations that need to appraise e-mail for long-term preservation and access on demand. Social implications - The research adopts an innovative approach to assist employees and records managers with the appraisal of digital records. By doing so, the research fosters new insights on the adoption of technological strategies to automate recordkeeping tasks, an important research gap. Originality/value - Our experiment show that a SVM classifier can be trained to replicate an expert's decision process for identifying e-mails of business value with a reasonably high degree of accuracy. In principle, such a classifier could be integrated into a corporate Electronic Document and Records Management System (EDRMS) to improve the quality of e-mail records appraisal.
机译：目的-本文旨在研究自动分类如何帮助员工和记录管理人员评估电子邮件，以评估组织的价值。设计/方法/方法-该研究对八位记录管理专家的评估行为进行了定性分析，以训练一系列支持向量机分类器，以复制用于识别业务价值电子邮件的决策过程。自动分类实验是对来自其中两个专家邮箱的846封电子邮件进行的。发现-尽管记录值具有高度上下文相关性，但这些实验表明分类器具有很高的准确性。与公司电子邮件归档中现有的手动做法不同，计算机分类模型并不高度依赖于诸如发送者和接收者的身份之类的功能，也不高度依赖于线程，转发或重要性标志。而是，主要的区分功能是电子邮件正文和主题字段中的文本功能。研究的局限性/意义-由于电子邮件仍然是记录保存的主要挑战之一，因此，对公司电子邮件进行自动分类的需求日益重要。实际意义-识别电子邮件记录的自动化方法有望为需要评估电子邮件以进行长期保存和按需访问的组织带来重大好处。社会影响-研究采用了一种创新的方法来协助员工和记录管理人员评估数字记录。这样一来，这项研究就可以采用新的技术策略来实现记录保存任务的自动化，这是一个重要的研究空白。原创性/价值-我们的实验表明，可以训练SVM分类器来复制专家的决策过程，以相当高的准确性来识别具有商业价值的电子邮件。原则上，这样的分类器可以集成到公司的电子文档和记录管理系统（EDRMS）中，以提高电子邮件记录评估的质量。

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