Assigning appropriate industry tag(s) to a company is a critical task in a financial institution as it impacts various financial machineries. Yet, it remains a complex task. Typically, such industry tags are to be assigned by Subject Matter Experts (SME) after evaluating company business lines against the industry definitions. It becomes even more challenging as companies continue to add new businesses and newer industry definitions are formed. Given the periodicity of the task it is reasonable to assume that an Artificial Intelligent (AI) agent could be developed to carry it out in an efficient manner. While this is an exciting prospect, the challenges appear from the need of historical patterns of such tag assignments (or Labeling). Labeling is often considered the most expensive task in Machine Learning (ML) due its dependency on SMEs and manual efforts. Therefore, often, in enterprise set up, an ML project encounters noisy and dependent labels. Such labels create technical hindrances for ML Models to produce robust tag assignments. We propose an ML pipeline which uses semantic similarity matching as an alternative to multi label text classification, while making use of a Label Similarity Matrix and a minimum labeling strategy. We demonstrate this pipeline achieves significant improvements over the noise and exhibit robust predictive capabilities.

在金融机构中，为公司分配适当的行业标签是一项关键任务，本文提出了一种基于语义相似度匹配的ML管道，通过使用标签相似矩阵和最小标记策略，实现了对噪声的显著改进和稳健的预测能力。

利用标签语义在嘈杂标记下提取公司与行业匹配的更高性能