While text mining and NLP research has been established for decades, there
remain gaps in the literature that reports the use of these techniques in
building real-world applications. For example, they typically look at single
and sometimes simplified tasks, and do not discuss in-depth data heterogeneity
and inconsistency that is common in real-world problems or their implication on
the development of their methods. Also, few prior work has focused on the
healthcare domain. In this work, we describe an industry project that developed
text mining and NLP solutions to mine millions of heterogeneous, multilingual
procurement documents in the healthcare sector. We extract structured
procurement contract data that is used to power a platform for dynamically
assessing supplier risks. Our work makes unique contributions in a number of
ways. First, we deal with highly heterogeneous, multilingual data and we
document our approach to tackle these challenges. This is mainly based on a
method that effectively uses domain knowledge and generalises to multiple text
mining and NLP tasks and languages. Second, applying this method to mine
millions of procurement documents, we develop the first structured procurement
contract database that will help facilitate the tendering process. Second,
Finally, we discuss lessons learned for practical text mining/NLP development,
and make recommendations for future research and practice.

这篇研究论文介绍了一项工业项目，该项目使用文本挖掘和 NLP 解决方案挖掘了医疗保健领域的数百万异构、多语言采购文件，提取了用于动态评估供应商风险的结构化采购合同数据，并发掘了处理这些挑战的方法以及未来研究和实践的建议。