Abstract
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.
Cite
CITATION STYLE
Symonenko, S., Rowe, S., & Liddy, E. D. (2006). Illuminating trouble tickets with sublanguage theory. In HLT-NAACL 2006 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Short Papers (pp. 169–172). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1614049.1614092
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
