Throughout the history of science, different knowledge areas have collaborated to overcome major research challenges. The task of associating a researcher with such areas makes a series of tasks feasible such as the organization of digital repositories, expertise recommendation and the formation of research groups for complex problems. In this article, we propose a simple yet effective automatic classification model that is capable of categorizing research expertise according to a knowledge area classification scheme. Our proposal relies on discriminatory evidence provided by the title of academic works, which is the minimum information capable of relating a researcher to its knowledge area. Our experiments show that using supervised machine learning methods trained with manually labeled information, it is possible to produce effective classification models.

中文翻译：

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在缺乏信息的情况下对研究专业知识进行分级分类的务实方法

在整个科学史上，不同的知识领域已经合作克服了主要的研究挑战。将研究人员与此类领域相关联的任务使一系列任务变得可行，例如数字存储库的组织，专业知识推荐以及针对复杂问题的研究小组的形成。在本文中，我们提出了一个简单而有效的自动分类模型，该模型能够根据知识领域分类方案对研究专业知识进行分类。我们的建议依赖于学术作品标题提供的歧视性证据，这是能够将研究人员与其知识领域联系起来的最低限度的信息。我们的实验表明，使用经过监督的机器学习方法（通过人工标记的信息进行训练），