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While text mining and NLP research has been established for decades, thereremain gaps in the literature that reports the use of these techniques inbuilding real-world applications. For example, they typically look at singleand sometimes simplified tasks, and do not discuss in-depth data heterogeneityand inconsistency that is common in real-world problems or their implication onthe development of their methods. Also, few prior work has focused on thehealthcare domain. In this work, we describe an industry project that developedtext mining and NLP solutions to mine millions of heterogeneous, multilingualprocurement documents in the healthcare sector. We extract structuredprocurement contract data that is used to power a platform for dynamicallyassessing supplier risks. Our work makes unique contributions in a number ofways. First, we deal with highly heterogeneous, multilingual data and wedocument our approach to tackle these challenges. This is mainly based on amethod that effectively uses domain knowledge and generalises to multiple textmining and NLP tasks and languages. Second, applying this method to minemillions of procurement documents, we develop the first structured procurementcontract 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.

Mining Healthcare Procurement Data Using Text Mining and Natural Language Processing -- Reflection From An Industrial Project