Measuring the impact and productivity of an author is an important, yet a challenging task. Most of the existing methods for ranking or indexing of authors are based on simple parameters such as publication counts, citation counts and their combinations. These methods are topic independent, hence ignoring the intra-field differences. This study introduces a specific method for indexing of researchers to measure their productivity in a given field of interest, believing that an author can be interested in more than one fields and can have different level of expertise in all these fields. This paper proposes Domain Specific Index (DSI), a novel method for indexing of authors with respect to their fields of interest. Latent Dirichlet Allocation (LDA) is applied to capture the latent topics within text corpora. DSI calculates the standing of an author in all topics of his or her interest by considering topic based citations instead of using overall citations like traditional methods. The citations received by a multi-authored paper are divided among all its co-authors on the basis of their topic probability in that particular field. Results show that instead of giving credit of received citations equally to all co-authors of a paper, if a weight is given with respect to their level of interest in that field, more specific authors in that field will be ranked as top authors.

中文翻译：

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根据作者的专业领域对作者进行索引

衡量作者的影响力和生产力是一项重要但具有挑战性的任务。大多数现有的作者排名或索引方法都基于简单的参数，例如发表次数、引用次数及其组合。这些方法与主题无关，因此忽略了字段内的差异。本研究介绍了一种特定的方法来索引研究人员，以衡量他们在给定兴趣领域的生产力，相信作者可以对多个领域感兴趣，并且可以在所有这些领域拥有不同水平的专业知识。本文提出了领域特定索引 (DSI)，这是一种根据作者感兴趣的领域对作者进行索引的新方法。应用潜在狄利克雷分配 (LDA) 来捕获文本语料库中的潜在主题。DSI 通过考虑基于主题的引用而不是像传统方法那样使用整体引用来计算作者在他或她感兴趣的所有主题中的地位。多作者论文收到的引文根据他们在该特定领域的主题概率在所有合著者之间进行分配。结果表明，如果根据他们对该领域的兴趣程度给予权重，而不是将收到的引用均等归功于论文的所有合著者，则该领域中更具体的作者将被列为顶级作者。