Illuminating Trouble Tickets with Sublanguage Theory

A study was conducted to explore the potential of Natural Language Processing (NLP)based knowledge discovery approaches for the task of representing and exploiting the vital information contained in field service (trouble) tickets for a large utility provider. Analysis of a subset of tickets, guided by sublanguage theory, identified linguistic patterns, which were translated into rule-based algorithms for automatic identification of tickets’ discourse structure. The subsequent data mining experiments showed promising results, suggesting that sublanguage is an effective framework for the task of discovering the historical and predictive value of trouble ticket data.