Industry assignment, which assigns firms to industries according to a predefined industry classification system (ICS), is fundamental to a large number of critical business practices, ranging from operations and strategic decision-making by firms to economic analyses by government agencies. Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, overlooking definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, ignoring the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability. We conduct extensive evaluations with two widely used ICSs and demonstrate the superiority of our method over prevalent existing methods.

中文翻译：

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利用专家知识将公司分配给行业：一种新颖的深度学习方法

行业分配是根据预定义的行业分类系统 (ICS) 将公司分配到行业，是许多关键商业实践的基础，从公司的运营和战略决策到政府机构的经济分析。三类专家知识对于有效的行业分配至关重要：基于定义的知识（即每个行业的专家定义）、基于结构的知识（即ICS中指定的行业之间的结构关系）和基于分配的知识（即，先前由领域专家执行的公司行业任务）。现有的行业分配方法仅利用基于分配的知识来学习将未分配的公司分类为行业的模型，而忽略了基于定义和基于结构的知识。而且，这些方法只考虑企业被分配到哪个行业，忽略了基于分配的知识的时间特异性，即分配发生的时间。为了解决现有方法的局限性，我们提出了一种基于深度学习的新颖方法，该方法不仅无缝集成了行业分配的三种类型的知识，而且还考虑了基于分配的知识的时间特异性。在方法论上，我们的方法有两个创新：动态行业代表性和层次分配。前者通过我们提出的时间和空间聚合机制整合三种类型的知识，将一个行业表示为一系列特定于时间的向量。后者以行业和公司代表作为输入，计算将一家公司分配到不同行业的概率，并将该公司分配到概率最高的行业。我们对两种广泛使用的 ICS 进行了广泛的评估，并证明了我们的方法相对于流行的现有方法的优越性。