Domain Knowledge Driven Key Term Extraction for IT Services

Prateeti Mohapatra, Yu Deng, Abhirut Gupta, Gargi Dasgupta, Amit M. Paradkar, Ruchi Mahindru, Daniela Rosu, Shu Tao, Pooja Aggarwal

Abstract: IT service support agents are trained on knowledge sources with large volumes of domain-specific documents, including product manuals and troubleshooting contents. Self-assist applications, such as search and support chat-bots must integrate such knowledge in order to conduct effective user interactions. In particular, the very large volume of domain-specific terms referenced in training documents must be accurately identified and qualified for relevance to specific context of support actions. We propose a weakly-supervised approach for extraction of key terms from IT support documents. The approach integrates domain knowledge to refine the extraction results. Our approach obviates the need for extensive expert work creating manual annotation and dictionary collection, as typically required in traditional supervised solutions, as well as the limited accuracy obtained in unsupervised methods. Results show that domain knowledge based refinement helps improve the overall accuracy of mined key terms by 25–30%.